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International Journal of Operational Research

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 International Journal of Operational Research (214 papers in press)  Regular Issues  The Operational Determinants of Hospitals Inpatients Departments Efficiency in Jordan   by Yazan Migdadi, Hala Al-Momani Abstract: The aim of this research is to examine the impact of hospitals operational factors on the efficiency of public hospitals inpatient departments in Jordan as a case in developing countries. Secondary data were collected from the annual statistical reports of the Ministry of Health. 15 out of 31 hospitals were surveyed, 9 out of 12 departments were investigated. DEA (CCR) input model was used to analyze the departments efficiency. Non parametric statistical techniques were used to analyze the relationship between variables. This study revealed that hospital's inpatients departments were categorised into extremely high efficient, high efficient, moderate efficient, low efficient, and extremely low efficient. no significant differences were found in efficiency among departments except ear nose and throat department with some departments, and ICU department with gynecology. Also, it was found that the location of hospital, capacity, and ALOS are not determinants for efficiency of all hospitals inpatients departments, occupancy rate is a determinant for some hospitals efficiency. The previous studies have focused mainly on reporting efficiency at hospital level with limited concern about reporting the departments efficiency and all of them have not investigated the impact of multi operational determinants on efficiency. Keywords: Efficiency; DEA; Average Length of Stay; Hospitals; Operations; Inpatients; Occupancy rate; Capacity; Location; Determinants. A proposed voting scheme to reduce the sensitivity of the Markov method   by Baback Vaziri, Yuehwern Yih, Tom Morin Abstract: The Markov method is one of many successful ranking methods that uses Markov chains to obtain its ratings and rankings of alternatives. It has been shown, however, that the method is sensitive to upsets, particularly in the tail of its ranking. The method also exhibits faulty behavior when it has a periodic Markov chain. This study proposes a modification to the voting scheme of the Markov method that will alleviate the sensitivity to upsets and remove the issue of periodicity in the Markov chain. To examine the sensitivity, we first provide an example and see how both voting schemes react to an upset. Next, we generalize both voting schemes and examine a ratio of rating increments to understand why the tailing effect occurs, and how we can subside its effect. Keywords: ranking methods; markov method; sensitivity; ranking vectors. Monte-Carlo Mirror Algorithm for the Port-of-Entry Inspection Problem   by Jorge Graneri, Sandro Moscatelli, Pablo Romero, Libertad Tansini, Omar Viera Abstract: A natural way to avoid the injection of potentially dangerous or illicit products in a certain country is by means of protection, following a strict port-of-entry inspection policy. A naive exhaustive manual inspection is the most secure policy. However, the number of within containers allows only to check a limited number of containers by day. As a consequence, a smart port-of-entry inspection policy must trade cost of inspection with security,in order to adapt the dynamic operation of a port. In practice, we are given a training set of containers with their risk classifications (i.e. labelled containers) provided by experienced port operators. The aim of this paper is to offer an automatic, simple and intuitive algorithm to select which containers should be inspected, following the given training set of classifications as close as possible. The classification problem is treated in this paper as a combinatorial optimization problem, and called from now on the Decision Problem. The purpose of the objective function is to obtain a classification as similar as possible to the one defined by the training set of labelled containers. The key is to obtain a representation of the knowledge of the operators as best as possible, by means of some emph{rule} or emph{law}, which is not defined directly by the labels of the containers. We prove that there exists an optimal deterministic inspection policy for the Decision Problem, called Mirror Solution. In real life, a deterministic approach for container inspection could be easily exploited by attackers using dynamic strategies. Inspired by the strength of Monte Carlo based methods for simulation of rare events, %and optimistic unchoking in peer-to-peer networks, we add randomization to the Mirror Solution. We first show that the randomized Mirror Solution is useful in practice and computationally efficient, since it depends linearly on the size of the training set, for a given number of sensors and risk levels. It statistically converges to the optimal deterministic solution when the training set converges to infinity. Finally, we present the results of the proposed port-of-entry inspection policy in a real-life scenario, regarding traces from the port of Montevideo, for containers with risk levels in {1,2,3}. There is a remarkable matching between the given classification and the one suggested by our algorithm. Moreover, the new policy checks random containers, and every container might be opened. As a consequence, there is no trivial strategy from attackers to outsmart the defense provided by the algorithm. Keywords: Risk analysis; Port-of-entry; Inspection policy; Monte Carlo. Simulation approach to solve fuzzy fixed charge multi-item solid transportation problems under budget constraint   by Pravash Kumar Giri, Manas Kumar Maiti, Manoranjan Maiti Abstract: In this paper, we have developed a dominate based genetic algorithm to solve fuzzy fixed charge multi-item solid transportation problems(FFCMISTPs) under budget constraint in fuzzy environment, in which sources, demands, capacities of conveyances, unit selling prices, unit purchasing costs, fixed charges, unit transportation costs and transportation times are fuzzy in nature. Here transportation problems are formulated in the form of profit maximization problems and solved. For maximization, a dominate based genetic algorithm (DBGA) with varying population size, cyclic crossover, two point mutation is developed which can deal with single-objective transportation problems. In the algorithm, fitness of a solution is taken as the ratio of the number of solutions in the population dominated by the solution and population size. After that lifetimes of chromosomes are set according to their fitness. A fuzzy rule base is used to determine fuzzy crossover probability of a pair of parents. The developed algorithm is tested against some test functions and its efficiency is established in terms of iteration numbers for single objective. The fuzzy objective function and constraints are reduced to corresponding deterministic ones using graded mean integrating value, possibility/necessity measures and chance constrained programming method. The reduced crisp problems are solved using developed genetic algorithm. The models are illustrated with numerical examples. The real life practical implication of the model is also presented. Keywords: Dominate base genetic algorithm; Solid transportation problem; Fixed charge; Budget constraints; Possibility/necessity measure; Graded mean integrated value criterion; Critical value criterion. Identification and prioritization of AIDA promotion model tools by use of fuzzy AHP approach   by Seyed Hojjat Bazazzadeh, Yazdan Shirmohammadi Abstract: In the modern literature of management, the responsibility of a manager is decision making. Identifying the best promotional mix element seems a crucial decision because in addition to satisfying the needs of the market, it should pay attention to the organizational objectives and marketing. Although a substantial amount of budget is devoted to promotion in the organizations, little attention paid to the effectiveness of the promotional methods and the desired result may not be achieved. So, before spending vast sums of money, best promotional mix tools should be identified in every industry based on its characteristics. In this study, we aim at helping managers in the tile and ceramic industry to identify and prioritize their promotion tools to improve their sales. AIDA model was used in this study and due to each element of AIDA, the best promotional mix tools were prioritized by FAHP. Keywords: promotion mix; tile and ceramic industry; AIDA; Fuzzy AHP. Minimization of total tardiness in hybrid flowshop scheduling problems with sequence dependent setup times using a discrete firefly algorithm   by Mariappan Kadarkarainadar Marichelvam Abstract: In this paper, hybrid flowshop (HFS) scheduling problems with sequence dependent setup times (SDST) are considered. The objective is to minimize the total tardiness. As these problems were proved to be strongly NP-hard (Non-deterministic polynomial time hard) type combinatorial optimization problems, exact methods cannot be used to solve the problems. Hence, many heuristics and meta-heuristics were addressed in the literature to solve the problems. In this paper, discrete version of a recently developed bio-inspired meta-heuristic algorithm called as discrete firefly algorithm (DFA) is proposed to solve the problems. Extensive computational experiments are carried out to validate the performance of the proposed algorithm. Computational results reveal the success of the proposed algorithm. Keywords: hybrid flowshop; scheduling; total tardiness; set up time; discrete firefly algorithm. A modified imperialist competitive algorithm for a two-agent single-machine scheduling under periodic maintenance consideration   by Maziyar Yazdani Abstract: Scheduling with periodic maintenance has been widely studied. However, multi-agent scheduling with simultaneous considerations of periodic maintenance has hardly been considered until now. In view of this, This research focuses on the problem of scheduling jobs that come from two agents on a single machine under periodic maintenance constraint with the objective of minimizing the total completion time of the jobs of the first agent while keeping the maximum tardiness of other agent below or at a fixed level UB. We present some new dominance properties for this strongly NP-hard problem. And next, using these properties, we develop a novel imperialist competitive algorithm for the problem. Various parameters of the Proposed algorithm are reviewed by means of Taguchi experimental design. For the evaluation of the proposed ICA, problem data was generated to compare it against a genetic algorithm. The results of computational experiments show the good performance of the proposed algorithm. Keywords: Scheduling ;Two agents ; Single machine ;Periodic maintenance ;imperialist competitive algorithm ; dominate properties. A Discrete SRGM for Multi-Release Software System with Faults of Different Severity   by Anu G. Aggarwal, P.K. Kapur, Nidhi Nijhawan Abstract: To meet highly competitive market challenges along with technological upgrades and changing user requirements, software firms offer newer versions of their products by adding new features and new functionalities. But this also results in increase in the fault content of software. In this paper we propose a discrete software reliability growth model (SRGM) for fault removal process incorporating the effect of up-gradations on the subsequent releases of the software. It is further assumed that faults present in the software are not of the same type and may be classified as simple and hard faults depending upon the effort and time consumed for their removal. The proposed model has been validated on real life data set for software with four releases. The results obtained are encouraging and fairly accurate. Keywords: Software reliability; discrete SRGM; fault removal process; multi-release software; simple faults; hard faults.DOI: 10.1504/IJOR.2018.10009423  A Predictive Analytic Approach to Determine Construction Cost Estimates   by Asil Oztekin, Rory Masterson Abstract: In the construction industry, cost estimates are the basis for which projects are awarded to suppliers. A cost estimate is arguably the most important inclusion in a suppliers response to a customers Request for Quote. The results of the estimates are presented to the potential customer for review and comparison to competitors estimates. In a circumstance that the customer favors an estimate and an award is issued, the estimate is then used to create a budget for the project. As such, it is essential for construction cost estimates to include features of accuracy, cost effectiveness, and profitability for the supplier. The amount of variables and qualitative considerations in an estimate require a methodology beyond an objective approach. A US Government Agency issues Tasks Orders on an annual basis that requires extensive estimation for a large number of projects in a very condensed period of time. Then a US Government service provider uses several suppliers to assist in creating these estimates. This paper uses data analytics to analyze both the US Government service providers internal estimate process and suppliers external estimate process to determine the best approach to use on future Task Orders. Three different classifier methods were used to perform the data analytic approach: MLP-based neural network, J48-based decision tree, and RBF-based support vector machine. Training and testing datasets were established using both random 66%-33% split and 10-fold cross-validation. Based on analyses performed for all three methods, RBF-based support vector machine with a 66%-33% random split and 10-fold cross-validation yielded the highest accuracy at 71%. Keywords: construction estimates; decision support systems; artificial intelligence; decision tree; neural networks; support vector machines; predictive analytics. A hybrid data envelopment analysis_decision tree approach To evaluate the bi-criteria flow shop with blocking problem   by Soulef Khalfallah, Zouhour Nabli Abstract: The purpose of this paper is to evaluate the overall performance of a bi-objective scheduling problem using a method based on a combination of Data Envelopment Analysis and Decision Tree. The hybrid approach is applied to the blocking flow shop scheduling problem with both makespan and total tardiness objectives. For this end, we propose two families of three-phase heuristics. In phase one, several constructive heuristics are used to generate initial solutions and in phase two, two improving heuristics are used to generate non-dominated solutions. One of the improving heuristics is based on the makespan criteria and the other one is based on the total tardiness criteria. The non-dominated solutions are then compared using the Free Disposal Hall (FDH) formulation of Data Envelopment Analysis (DEA). The overall performance of the composed heuristics is measured using a decision tree approach. Keywords: Bi-criteria; Scheduling; Blocking; Makespan; Total tardiness; DEA; Data Envelopment Analysis; Decision Tree; Non-Dominated solutions. Developing new methods for determining weights of components in network data envelopment analysis   by Reza Farzipoor Saen, Hojatollah Rajabi Moshtaghi, Gholam Reza Faramarzi Abstract: Data envelopment analysis (DEA) is a powerful tool for measuring relative efficiency of decision making units (DMUs). In many cases such DMUs have network structures with internal structures. Traditional DEA models, however, consider DMUs as black boxes without considering their internal structures. Furthermore, overall efficiency in multi component networks is based on efficiencies of their components. Cook et al. (2010) used additive weighted average of components efficiencies to calculate overall efficiency. They used ratio of total weighted input of component to total weighted input of whole components as a weight of component. As an alternative approach, Faramarzi et al. (2014) proposed that the weights are ratio of total weighted output at the ith component to total weighted output of whole components. In this paper, we propose three novel methods to obtain the weights of components. Then, to compare these three new methods and the methods proposed by Cook et al. (2010) and Faramarzi et al. (2014), we present a case study. Finally, using Spearmans rank correlation coefficient, we analyze correlation among different approaches. Keywords: Network data envelopment analysis; Refineries; Multi component network; Spearman’s rank correlation. Supply Chain Coordination with Multiple Retailers and Non-Linear Production Costs   by Yigal Gerchak Abstract: We consider coordination with multiple retailers when supplier\'s production costs are non-linear. Microeconomists typically assume increasing marginal production costs (i.e., convex production costs). The non-linearity of the production costs introduces indirect dependence among the retailers, as the order quantity of one influence the cost of producing the othe\'s lot.We consider a fixed retail price scenario, as well as price-dependent linear additive and iso-elastic demands, We explore there scenarios under wholesale-price-only contract, as well as revenue sharing contract. Keywords: coordination; non-linear costs; multiple retailers. MAXIMIZING THE NUMBER OF ON-TIME JOBS ON PARALLEL SERVERS WITH SEQUENCE DEPENDENT DETERIORATING PROCESSING TIMES AND PERIODIC MAINTENANCE   by Eduardo Pérez, Rahul Ambati, Alex J. Ruiz-Torres Abstract: This paper considers a parallel-machine scheduling problem with sequence dependent processing times and periodic maintenance. The time to complete jobs increases as the machines deteriorate and the machine deterioration depends on the particular job sequence assigned to a machine. The planned maintenance activity returns the machine to its optimal condition, and all machines undergo this maintenance activity at the same time. The objective is to find the job schedule that maximizes the number of on time jobs given a specified maintenance schedule. The paper presents a mathematical programming formulation, several solution algorithms, and evaluates their performance under various experimental conditions. Keywords: Parallel machines; machine deterioration; late jobs; on-time jobs; scheduling; maintenance. Capacity-Delivery Coordination in Supply Chains A Cost-based Approach   by Lihua Chen, Alfred Guiffrida, Pratim Datta Abstract: We develop a cost-based model considering a two-stage supply chain with a buyer ordering from a supplier. The model is constructed to coordinate the supply chain by joint decisions over delivery performance and capacity management. In this model, the supplier randomly assigns a portion of his permanent capacity to the buyer and the buyer determines his order quantity accordingly. A delivery window is used to classify deliveries as early, on-time or late and is used to evaluate the delivery performance of the supplier when delivery time is governed by a truncated normal probability density function. Numerical sensitivity analyses are presented to illustrate the significant managerial implications of this model. Keywords: Supply Chain Coordination; Delivery timeliness; Capacity Allocation. Determining k - Possible Critical Paths Using Tawandas Non-iterative Optimal Tree Algorithm for Shortest Route Problems   by Trust Tawanda Abstract: The critical path method (CPM) is a project modelling algorithm developed in the 1950s for scheduling project activities, it is used to determine the critical path through the calculation of three parameters thus, slack, earliest event, latest event times for each activity. In this paper, we demonstrate how to use Tawandas non-iterative optimal tree algorithm for shortest route problems (TA) to determine the critical path(s). We have also compared TA with the original Critical path method (CPM) and the Modified Dijksras algorithm for critical path method in a project network (MDA). However the study revealed that TA can compute the critical path more effectively since it is also effective in project networks with k possible critical paths, moreover it doesnt make use of the slack, earliest, and latest time parameters, since these calculations consume more time. Keywords: Dijkstra’s algorithm; Critical path method; Critical path; Project network; Graph expansion; Slack time. Production Inventory Model with Reworking of Imperfect items and Integrates Cost Reduction delivery policy   by Sanjai M., Periyasamy S. Abstract: The Economic Production Quantity (EPQ) model is commonly used by practitioners in the fields of production and inventory management to assist them in making decision on production lot size. The classical EPQ model assumes that all items manufactured are of perfect quality. However, in real life production systems, due to various controllable and /or uncontrollable factors the generation of defective items during a production run seems to be inevitable they should be reworked. A portion of non-conforming items produced is considered to be scrap, while the rest are assumed to be repairable and are reworked in each cycle when regular production ends. This paper integrates cost reduction delivery policy into production inventory model with defective items with scrap and rework and finished items can only be delivered to customers at a fixed interval of time during production downtime with the purpose of reducing holding cost. The rate of reworking is . The objective is to derive the optimal production lot size and the number of deliveries. A suitable mathematical model is developed and the optimal production lot size which minimizes the total cost is derived. The relevant model is built, solved and necessary and sufficient conditions for optimal solution are derived. An illustrative example is provided and numerically verified. The validation of result in this model was coded in Microsoft Visual Basic 6.0. Keywords: Inventory; Production; Defective Items; delivery policy; rework; scrap; demand and production. Quantifying the benefits of lean thinking adoption by the beer game supply chain.   by Rossella Pozzi, Fernanda Strozzi, Tommaso Rossi, Carlo Noè Abstract: Benefits obtained by the adoption of lean thinking are often declared in the literature but not measured both at local and global level. In this work the benefits quantification of an adoption of a lean thinking order policy is measured in the case of Beer Game supply chain modelled by Thomsen et al. (1992) considering the impact on bullwhip effect and inventory both on a supply chain level and on a single stage level. The paper shows that lean thinking provides major benefits to the whole supply chain in terms of bullwhip effect and carried inventory if more and more levels apply it. On the other hand, the paper highlights that these benefits not always hold locally i.e. for some levels can be more profitable when no levels apply an order policy lean thinking based. Keywords: Lean thinking; Bullwhip effect; Beer Game.DOI: 10.1504/IJOR.2018.10002669  STOCHASTIC ANALYSIS OF A BASE TRANSCEIVER SYSTEM CONSIDERING TRAFFIC CONGESTION AND CHANCES OF HARDWARE/ SOFTWARE EXPANSIONS   by Sunny Kapoor, Rajeev Kumar Abstract: Base transceiver station is a critical part in a mobile communication system and its performance and cost play a significant role in network survivability and communication. The paper proposes a stochastic model for a base transceiver station that may encounter hardware and software faults, traffic congestion and common cause failures. The possibility of occurrence of hardware based software failure is also considered. On failure of the system, the technicians first inspect whether there is hardware or software or hardware based software fault and then carries out recovery of the relevant component. In the system there may be minor or major hardware/software faults or common cause failure where a minor fault leads to degradation whereas a major fault/ common cause failure leads to complete failure of the system. Further, network traffic congestion is considered to be automatically removed and in case not, the technician will inspect the system for the purpose of hardware or software expansions. Using Markov process and regenerative point technique, various measures of system performance are obtained. On the basis of these measures the profit analysis of the system is also carried out. Various conclusions about reliability, performance and profit of the system are made on the basis of the graphical studies. Keywords: base transceiver station; hardware based software fault; common cause failure; mean time to system failure; expected uptime/ degradation time; expected congestion time; hardware expansion; software expansion; profit; Markov process and regenerative point technique.DOI: 10.1504/IJOR.2018.10009552  Waiting Time Distribution for an Exchangeable Item Repair System with Two Failed Components   by Michael Dreyfuss, Alan Stulman Abstract: Models involving exchangeable component repair systems are widely treated in the literature. In such systems a customer arrives at a repair queue with a failed component which is replaced from stocks of previously repaired components. Various strategies and service measures have been discussed. The waiting time distribution for a single failed component has also been developed. Most customers who arrive at a repair facility will arrive with a single failed component type. They will be referred to the appropriate service queue which will ultimately exchange that particular failed component. Due to its complexity the development of the waiting time distribution for a single queue servicing multiple simultaneous component failures has been generally neglected. However, there are many instances where two distinct components may be somehow linked so that the failure of one will cause the failure of the second. Whether a customer arrives with a single failed component or with two distinct failed components finding the waiting time distribution is important because it can lead to better facility planning and new realistic service measures. For example, how many extra spares should be added to the system so that the probability of waiting more than an acceptable amount of time (the fill rate window) could be kept to within predefined limits. In this paper we limit our mathematical development to the not uncommon problem of two simultaneous failed components but the ideas developed may be extendable to more than just two failed component types. Keywords: Spares; queues; exchangeable-item repair system; fill rate window; ample servers.DOI: 10.1504/IJOR.2018.10004560  Simultaneous Optimization of Forward and Reverse Distribution Processes with Multiple Types of Reuse within an Industrial Tool Supply Chain   by Martin Steinrücke, Michael Jahr Abstract: In this article we present a quantitative approach for the simultaneous coordination of forward and reverse material flows which is motivated by a real-life case of an international supply chain network in the industrial tool industry. The objective is to optimize the quantitative supply chain network processes related to procurement, production and transportation. Moreover, we consider multiple types of reuse, including remanufacturing and recycling processes, in a medium-term planning horizon. To model the three echelon production-distribution network, mixed-integer-linear-programming is used as it offers a modular modelling technique. The implementation of a mathematical optimal plan in a complex business network structure requires a big-bucket model solution to grant the plans stability via sufficient time buffers. The contribution of the paper can be found in the combined remanufacturing and recycling in a quantitative planning model, when the interdependencies between new and used products are relevant and significant. Keywords: Transportation, Distribution and Logistics; Procurement/Purchasing Processes; Supply Chain Management; Mathematical Modelling Using Heuristic MCMC Method for Terminal Location Planning in Intermodal Transportation   by Xiaobin Wu, Lei Cao Abstract: In this paper, we consider the planning of terminal locations for intermodal transportation systems. With a given number of potential locations, we aim to find the most appropriate number of those as terminals to provide the economically most efficient operation when multiple service pairs are needed simultaneously. The problem also has an inherent task to determine the optimal route paths for each service pair. For this NP-hard problem, we present a Markov Chain Monte Carlo (MCMC)-based two-layer method to find a suboptimal solution. In the lower-layer, the routing for all service pairs given a particular location planning is solved through a table-based heuristic method that considers both efficiency and fairness. In the upper-layer, by mapping the cost function into a stationary distribution, the optimal planning is solved based on a MCMC method that integrates advantages of both simulated annealing and slice sampling. Finally, the effectiveness of this Heuristic MCMC-based method is demonstrated through computer experiments. Keywords: Heuristic optimization, Markov Chain Monte Carlo (MCMC), Multimodal transportation, Terminal location GTA based Framework for evaluating the effectiveness of material handling equipments in FMS environment   by Surinder Kumar, Tilak Raj Abstract: In this paper, a methodology to evaluate the effectiveness of material handling equipments variables using graph theoretic approach (GTA) is presented. Variables affecting the effectiveness of material handling equipments and their interactions are analysed by developing a mathematical model using graph theoretic approach. Permanent function performance index is obtained from the matrix model developed from the digraph. This permanent function value ranks the equipment variables which help in selection of optimum process parameters during flexible and automated material handling system adoption. Keywords: GTA, material handling, equipment, variables, digraph, effectiveness. Imperfect Production system with Rework and Scrap at a single stage manufacturing system and Integrates Cost Reduction Delivery Policy   by Selvaraju Palanisamy Abstract: This paper deals with an Production inventory model with reworkable defective items and integrates a cost reduction delivery policy is used. In this work, it is assumed that in each cycle the rework process of all defective items starts when the regular production process finishes. After the rework process, a portion of reworked items fails and this portion becomes scrap. In this paper, two models are developed, in the first model, the perfect finished items can be delivered to customers at the end of rework process and in the second model, integrates cost reduction delivery policy and finished items can only be delivered to customers at a fixed interval of time during production down time with the purpose of reducing holding cost. The objective is to derive the optimal production lot size, number of deliveries and the finished items are delivered by request to customers at a fixed interval of time that minimizes total costs. The relevant model is built, solved and necessary and sufficient conditions for a unique and global optimal solution are derived. An illustrative example is provided and numerically verified. The validation of result in this model was coded in Microsoft Visual Basic 6.0. Keywords: EPQ model, optimum quantity, periodic deliveries, production, rework, scrap, defective rate. Vehicle Routing Problem: Recent Literature Review of its variants   by Satyendra Kumar Sharma, Utkarsh Yadav, Srikant Routroy Abstract: The Vehicle Routing problem is the most studied combinatorial optimization problem. The purpose of this study is to provide an overview of the research to date in vehicle routing problem variants. The literature is reviewed with a focus on research in three major variants of the Vehicle Routing problem namely capacitated vehicle routing problem, mixed depot vehicle routing problem and vehicle routing problem with pickup and delivery. Journal articles from three academic databases namely Taylor and Francis, Elsevier and Emerald are selected and reviewed. Since ample literature is available on this problem so to restrict the scope, we screened the journal articles using the above mentioned variants precisely, excluding those that are in combination with other variants. This review takes a closer look at 117 research articles selected from various journals. By presenting the past literature, we hope to motivate further research in the field. Keywords: Vehicle routing; Algorithms; Literature survey. A MIXED-INTEGER LINEAR FORMULATION FOR A CAPACITATED FACILITY LOCATION PROBLEM IN SUPPLY CHAIN NETWORK DESIGN   by Duong Vo Hung, Bui Nguyen Hung Abstract: In this research, we deal with a multi-item, multi-period, two-echelon capacitated facility location problem. With every period in horizon planning, manufacturing plants and distribution centers are decided to open or not at predetermined potential sites. The developed model is formulated as a mixed integer linear programming model (MILP) with the objective minimizing the total cost, including transportation cost, inventory holding cost, and fixed costs for opening facilities. We employ a Lagrangian relaxation algorithm for solving the developed model. Before decomposition into sub-problems, the initial structure of developed model is modified, three additional constraint sets add to two sub-problems, and these are the key differences of our algorithm. For validation testing, some numerical experiments are used for solving, and the solutions obtained from the Lagrangian relaxation algorithm are respectively compared with the solutions obtained by the LINGO solver. With good achievements of this research, our proposed model can be applicability and the proposed approach is advantage for getting the specific solutions. Keywords: logistic; supply chain; mixed integer linear programming; Lagrangian relaxation; network design. A Single server non-Markovian retrial queue with two types of service and Bernoulli vacation   by D. Arivudainambi, Mahalingam Gowsalya Abstract: A single server retrial queueing system with two types of service and Bernoulli vacation is analyzed in this paper. It is assumed that the arriving job finds the server busy by providing either type of service is said to be in orbit with an FCFS discipline and repeat its request (demand) for service after some random time. The customer at the head of the orbit is allowed to access the server. For such queueing model, the system size probabilities are investigated in steady state by using supplementary variable technique. The effects of various parameters on the system performance are analyzed numerically. Stochastic decomposition and some special cases of interest are also discussed. Keywords: Retrial queues; Two types of service; Bernoulli vacation; Steady state; Stochastic decomposition. Joint replenishment models with ramp demands and price dependant substitute ratio during Stock-out   by Raghu Giri, Shyamal Mondal, Manoranjan Maiti Abstract: The paper deals with a single period joint replenishment model (JRM) of two substitutable items with ramp type demands in an inventory system. Here customers demands are dynamic, quadratic function of time, t. It is assumed that when an item is out of stock, demand of this item is partially met by the other available substitute item on the basis of items prices difference. A new substitute function is presented here for this purpose. We\r\ndetermine the optimal order quantities of each item for maximum average total profit. Also attention is paid to maintain a balance between stocks of the itemsi.e. not to have too much or very insufficient stock of the items at the end of a cycle. The models are illustrated with numerical data, some sensitivity analyses and managerial insights are presented. Results of\r\nseveral particular models including the correct results of Salameh et al.s model are derived from the general model. Keywords: Joint replenishment model; ramp demand; substitute items; substitute ratio. Milk procurement of a private dairy firm: an economic analysis   by Priyanka Chaturvedi, Ashutosh Sarkar, Gautam C. Majumdar, Sarada P. Sarmah, Sidhartha S. Padhi Abstract: In this paper, we study the milk procurement system of a private dairy firm that buys milk from two channels, namely from farmers and through intermediaries. The collected milk is processed into value-added products like, butter, ghee, yoghurt etc. In India, milk is predominantly collected through cooperatives and most often the farmers are stakeholders of such cooperatives. Further, the intermediaries also sell their milk to the local market and hence, behave opportunistically. Therefore, availability of milk for the private dairy firm becomes critical and depends on market principles. The game played between the intermediary and the dairy has been modelled as a Stackellberg game and expressions for their optimal decisions are obtained. Based on data collected from a private dairy firm, we carried out numerical analysis to understand the behaviour of the intermediary and the dairy firm. Keywords: Supply chain; Procurement; supply chain coordination; supply contract.DOI: 10.1504/IJOR.2018.10001109  A Probabilistic Model for Maintaining and Optimizing the Life-cycle Performance of Deteriorating Structures   by Reza Ahmadi Abstract: Benefiting from a devised imperfect repair model and given a cost structure, this paper addresses the problem of determining an optimal inspection and threshold-type repair policy for systems whose performance is described by a Wiener process. The system is monitored at periodic times and preventive maintenance actions are carried out in response to the observed system state. The approach can deal with failures defined by performance or regulations. Precisely speaking, failure is defined by a critical set such that the first entry of the performance process to the critical set implies system failure. This approach is typically appropriate for lifecycle models as a specific performance requirement is, or is close to being violated. Since there is a random amount of maintenance, and on the other hand each maintenance incurs a cost, using the renewal-reward theorem, this paper aims at joint determination of an optimal inspection and repair policy providing a right balance between the amount of maintenance and the increasing cost. Benefiting from an imperfect repair model, the presented probabilistic modelrnprovides a framework for further developments. Keywords: Maintenance; Inspection; Imperfect repair model; Integral equations; Renewal reward theorem. A Global Dichotomous Search-Based Heuristic for the Three-Dimensional Sphere Packing Problem   by Mhand Hifi, Labib Yousef Abstract: In this paper we propose a global dichotomous search-based heuristic for solving the three-dimensional sphere packing problem. In the sphere packing problem, we are given a set of predefined unequal spheres and a large container with unlimited length. The goal of the problem is to determine the minimum length of the container that contains all spheres without overlapping. We propose to optimize the length of the large container by applying a truncated tree-search that combines a hill-climbing strategy, a hybrid operator that combines both priority and total-cost operators and, a dichotomous interval search in order to diversify the search space. Further, in order to enhance the quality of solutions of internal nodes, a local dichotomous search is applied almost of using a descent method. The proposed method is evaluated on benchmark instances taken from the literature and its provided results are compared to those reached by recent published methods in the literature. The proposed method is able to improve most solutions available in the literature. Keywords: Dichotomous; Heuristic; Hill-Climbing; Optimization; Packing; Tree-Search. A production - Inventory model with stochastic lead time and JIT set up cost   by Ruchira Chakrabarty, Tapan Roy, Kripasindhu Chaudhuri Abstract: We developed this inventory model under price dependent demand in stochastic environment. Here probabilistic lead time is considered and shortages are allowed (if occur) over a finite time horizon. Generalizing the work of Maiti et al. [1] more precisely, we consider this model with Just-in-Time set up cost where deterioration is taken into account and backlogging rate has been considered as a negative exponential function of the waiting time. Taking all these into account, mathematical expression for expected average profit is derived. A closed form of analytic solution for maximizing the expected average profit function is obtained when demand is constant. Numerical examples are carried out to identify the most sensitive parameter. Keywords: Inventory; stochastic lead time; JIT set- up cost; deterioration; partial backlogging; price dependent demand. Impact of Inventory Cannibalization on a Retailer Selling Substitutes   by Chirag Surti, Prakash Abad, Elkafi Hassini Abstract: The reasons customers substitute, are well understood from an economic perspective. However, its exact impact on retailer's inventory and product is not, when customers substitute as a result of a shortage. Shortage of one product may lead to demand spillover, due to substitution, resulting in shortages for the second product. We call this inventory cannibalization. This is a store level, retailer observed phenomenon that is a direct result of customers willingness to switch between substitutes due to stockout of one product. Many retailers are experiencing stockouts related to substitution, resulting in a significant loss of revenue. We model a retailers selling two substitutes, facing price-sensitive stochastic demand. Our model incorporates cannibalization explicitly and generalizes the existing literature on inventory substitution. We perform analytical and numerical analysis to study the impact of stockout based substitution and the related inventory cannibalization on retailers decisions. We find that the impact of cannibalization is felt most acutely by the retailer for products with low degree of substitution. Keywords: Pricing; Stock Outs; Substitution; Inventory Management; Inventory Cannibalization; Retail Operations. MULTIOBJECTIVE OPTIMIZATION MODEL FOR THE SELECTION OF CRITICAL SUPPLIERS INTEGRATING SUSTAINABILITY CRITERIA   by Aineth Torres-Ruiz, A.R. Ravindran Abstract: Most companies are seeking their supplier base around the world. However, sourcing from a global supply base exposes buying companies to a notable set of risks and naturally increases transportation distance with the associated environmental consequences. In this study, we propose a multiobjective order allocation model for selecting primary and backup suppliers in a global supply chain setting. Our model explicitly minimizes product costs, transportation costs and the cost of exceeding CO2 allowances within total procurement cost, while also minimizing lead-time, sustainability risks and greenhouse gas (GHG) emissions. We present a case study where our model is applied to a global manufacturer of consumer goods. The current supply scenario is compared against an optimal scenario given by the proposed model. Although total procurement cost is assigned the highest priority, the optimal scenario (which assigns orders to primary suppliers locally located) represents important advantages in relation to all the criteria evaluated. Keywords: supply risk; multiobjective optimization; goal programming; global supply chain; supplier selection; green procurement; sustainable procurement; GHG emissions. An Efficient Methodology for Robust Assignment Problem   by Kais Zaman, Subrata Saha Abstract: This paper proposes formulations and algorithms for assignment problem under natural or physical variability, from the perspective of robustness of the assignment. We formulate the robust assignment problem as a non-linear binary programming problem. An equivalent linear binary programming formulation and a modified Hungarian approach are then proposed to achieve computational efficiency. The proposed methods are illustrated for two example problems, where the information on the problem parameters is available as their means and standard deviations. Keywords: Assignment problem; Robust optimization; Uncertainty; Multi-objective optimization. Searching for optimal positions through directional data in a political competition model   by Mariló López, Javier Rodrigo, Sagrario Lantarón Abstract: In this paper we develop an application of directional data to political science. We present a model in which the political preferences of the type of voters of a population are represented as points of the circumference unit and the political parties search for optimal positions on it in order to win the highest support of that finite set of types of voters. We develop strategies to search for optimal positions in the case of one party and Nash equilibrium positions in the game with two parties.rnThe obtained results show the different parties how to redirect their stances to adapt themselves to changing situations generated by economic or social circumstances which affect the preferences of citizens. Keywords: Political Competition; Game Theory; Optimisation; Nash Equilibrium; Applications of OR techniques; OR models; Geometric programming; Exact algorithms. Analysis of Results Obtained on Return Contracts with Warranty   by Shirsendu Nandi Abstract: Literature on supply chain contract has extensively dealt with the coordination models with respect to different supply chain contracts and different properties and results obtained in the said context. The current study obtains results for coordination involving different contract parameters, exogenous variables when a warranty clause is also considered along with two return policy contracts viz: buyback contract and quantity flexibility contract. It also investigates whether the key properties hold good in this extended model where warranty related variables are also considered. The study provides a guideline to the channel coordinator to optimally design the contract parameters and warranty length to achieve channel coordination and optimal supply chain profit. The model is built considering a two stage supply chain and is applicable to different demand distributions. The theoretical results discussed in the present study can be suitably used in designing contracts and warranty parameters in different supply chains. Keywords: Buyback; Quantity flexibility; Supply chain coordination; Warranty; Supply Chain Contract. Study on the three players linear rendezvous search problem   by Mohamed Abd Allah El-hadidy Abstract: This paper considers the symmetric rendezvous problem of three players on the line. This problem asks how the players forced to use the same mixed strategy, can minimize their expected meeting time. This minimum is called the "symmetric" rendezvous value of the line. In our problem we consider the effect on rendezvous time of giving the players some information about past actions and chance moves, enabling each of them to apply Bayesian updates to improve the knowledge of the others whereabouts. This technique can be used to give lower bounds on rendezvous time of the original game (without any revealed information). Our approach is to concentrate on a general analysis of the effect of revelations, rather than compute the best bounds possible with our techniques. Keywords: rendezvous search problem; mixed strategy; Bayesian updates. An economic production lot size model for randomly imperfect production system with stock-dependent demand and rework.   by Manoranjan De, Barun Das, Manoranjan Maiti Abstract: This paper considers a single item, imperfect economic production lot-size (EPL) model with stock-dependent demand and partial rework. In real life EPL models, defective production commences from the out-of-control state, after the passage of some time from production commencement. Its occurrence is random after the lapse of certain time and imposed here through a chance constraint. The set-up cost is partly production dependent. Unit production cost is also production dependent and a part of it is taken as environment protection cost. Defective rate is also assumed to be random and production dependent. The model is formulated as an average cost minimization problem subject to a chance constraint and solved using a non-linear optimization technique- Generalized Reduced Gradient method (GRG) through LINGO 11.0. Several special cases are derived and more speci cally, the present investigation the works of Sana (2010) and Khouja and Mehrez, (1994). Numerical experiments are performed to illustrate the general and particular models. Some sensitivity analyses are presented against few model parameters. Keywords: Imperfect production system; Stock-dependent demand; Random defective rate, Chance constraints; Environment protection cost. Metasearch aggregation using linear programming and neural networks   by Sujeet Kumar Sharma, Srikrishna Govindaluri, Gholam R. Amin Abstract: A metasearch engine aggregates the retrieved results of multiple search engines for a submitted query. The purpose of this paper is to formulate a metasearch aggregation using linear programming and neural networks by incorporating the importance weights of the involved search engines. A two-stage methodology is introduced where the importance weights of individual search engines are determined using a neural network model. The weights are then used by a linear programming model for aggregating the final ranked list. The results from the proposed method are compared with the results obtained from a simple model that assumes subjective weights for search engines. The comparison of the two sets of results shows that neural network based linear programming model is superior in optimizing the relevance of aggregated results. Keywords: Metasearch; Search engine; Data aggregation; Linear Programming; Neural networks. Healthcare Waste Management Practices' Identification and Evaluation to Rank Hospitals   by Ankur Chauhan, Amol Singh, Sanjay Jharkharia Abstract: In the present study, various criteria (practices) have been identified from the literature of healthcare waste management (HCWM). The analytic hierarchy process (AHP) has been applied to compute the weights of these criteria. The weight of a criterion shows its significance for healthcare waste management. Furthermore, the weights of criteria have been used as an input to Technique for order preference by similarity to ideal solution (TOPSIS),for ranking six alternatives (hospitals) to demonstrate the assessment methodology in real time. The literary contribution of this work is the identification of HCWM practices, which can be very useful in the assessment of the hospitals waste management planning, shown by the case study.rn Keywords: Healthcare waste management; Policy Making; MCDM; AHP; TOPSIS. LR-optimal solution of nonlinear optimization problem with varying parameters   by Mrinal Jana, Geetanjali Panda Abstract: In this paper a nonlinear optimization problem is studied in uncertain environment. The objective function and constraints of this problem are interval valued functions. Solution of the problem is defined with respect to $LR$-partial order relation, andrnmethodology is developed to derive these solutions. The proposed methodology is illustrated through numerical examples. A possible application of the optimization model in finance is described at the end. Keywords: Nonlinear optimization problem; Interval analysis; Interval valued function; Partial order relation; Decision making problem. An inventory model of deteriorating items with a credit period based new demand function in a finite time horizon.   by Ateka Banu, Shyamal Kumar Mondal Abstract: In today's competitive market, the trade credit plays an important role to in-rncrease demands of customers/buyers. Here, we consider a two-level trade credit policy in which a delay in payment is offered by supplier to retailer and also an another delay in payment is offered by retailer to his/her all customers. In this model, it is proposed that the demand function is dependent on the length of the customer's credit period and also the duration of offering the credit period. The purpose of this model is to establish a deterministic EOQ model of deteriorating items for the retailer to maximize the total profit and the number of replenishment cycle in finite time horizon. We develop an algorithm to find out the optimal solutions. Also the model is explained with the help of numerical examples and sensitivity analysis are given to illustrate the features of the proposed model with respect to some parameters. Keywords: Inventory; Deterioration; Two-level credit financing; Credit period dependent demand; Inflation. A Tabu Search Algorithm for a Capacitated Clustering Problem   by Sudha Khambhampati, Prasad Calyam, Xinhui Zhang Abstract: This paper investigates a clustering problem in a production and routing environment where a centralized facility uses a fleet of vehicles to serve a set of customers. A multi-period capac- itated clustering problem is solved to partition customer into clusters with constraints that the accumulated customer demand in every period of the planning horizon is satisfied. The resulting mathematical model is hard to solve exactly and efficient heuristics are thus developed in this paper. The heuristics are based on the tabu search but include two novel features in the design of neighborhood search; the first one is the dynamic combination of customers with close proximity into supernodes to eliminate ineffective moves and the second one is an ejection chain approach to perform composite moves of variable length from a series of simple moves. Computational results showed that they were able to provide superior solutions, especially in certain cases of tight capacity constraints. Keywords: Capacitated Clustering; Heuristics,Supernode,Ejection Chain; Tabu Search. An EOQ Model for Deteriorative items with Constant and Linear and quadratic Holding cost and shortages- a comparative study   by Selvaraju Palanisamy Abstract: In the classical inventory model the holding cost was assumed to be constant. However, in reality holding cost may not always be constant. The holding cost may be time dependent. In this paper, a production inventory model with deteriorating items with constant, linear and quadratic holding cost is considered and also a comparative study is carried out between constant holding cost and time dependent (linear and quadratic) holding costs. Three models are developed. In three models, the optimum time and total cost are derived when the holding cost is (i) constant, (ii) linear; (iii) quadratic. A mathematical model is developed for each model and the optimal production lot size which minimizes the total cost is derived. The optimal solution is derived and an illustrative example is provided. The validation of result in this model was coded in Microsoft Visual Basic 6.0 Keywords: Inventory; Deteriorating; linear; quadratic; holding cost; demand and production. The Distribution Free Newsboy Problem with Partial Information   by Krishna Sundar, K. Ravikumar, Siddharth Mahajan Abstract: We present a new ordering rule for the newsboy problem where besides the mean and variance of demand, the probability that the demand assumes the value zero is known. We derive a lower bound for the expected profit over the set of distributions with given parameters and construct a distribution which achieves the bound. We apply our analysis to an M/G/1 queue with server vacations, which is the base model for many production-inventory systems. Keywords: Distribution free Newsboy problem; Inventory Control; M/G/1 queue; Server vacations. A MEMETIC ALGORITHM FOR THE GENERALIZED MACHINE LAYOUT PROBLEM   by Juan Jaramillo, Alan McKendall Abstract: Designing efficient machine layouts is key to ensure profitability in manufacturing environments. The major decisions in designing machine layouts are: the selection of machines (including machine replicas); the assigning of machines to the plant floor; the selection of production mix (i.e., products to be produced); and the assigning of products to machines (i.e., product flows). The Generalized MAchine Layout Problem (GMALP) integrates these factors under a single problem. This works presents a memetic algorithm for the GMALP. The memetic algorithm takes advantage of the diversification strengths of the genetic algorithm combined with the intensification abilities of tabu search. Results obtained with the memetic algorithm compares favorably with results presented in the literature. Keywords: memetic algorithm; GMALP; generalized machine layout problem; MLP; machine layout Problem; evolutionary algorithms; tabu search; TS. Different hydraulic analysis conditions for sewer network design optimization problem using three different evolutionary algorithms   by Ramtin Moeini Abstract: In this paper, the efficiency of considering the constant and varying Manning coefficient for a hydraulic analysis model on the optimal solution of sewer network design optimization problem is studied. To solve sewer network design optimization problem, here, different formulations are proposed using genetic algorithm, discreet and continues ant colony optimization algorithms. In all proposed formulations, the nodal cover depths of the sewer network are taken as decision variables of the problem. Furthermore, for both ant-based algorithms two different formulations are proposed using unconstrained and constrained versions of these algorithms. The constrained versions of these algorithms are proposed here for the explicit satisfaction of the minimum pipe slope constraint leading to smaller search space. Two benchmark test examples are solved here using proposed formulations and the results are presented and compared with other available results. Comparison of the results shows the superiority of considering varying Manning coefficient condition for hydraulic analysis model. Furthermore, the results show the superiority of continues ant colony optimization algorithm and especially the constrained version of it to optimally solve the sewer network design optimization problem. Keywords: Manning coefficient; hydraulic analysis model; sewer network; Genetic Algorithm; Discrete Ant Colony Optimization Algorithm; Continuous Ant Colony Optimization Algorithm. Supply chain delivery performance improvement for several delivery time distributions   by Maxim Bushuev, Alfred Guiffrida, Tatiana Rudchenko Abstract: This paper investigates strategies for improving supply chain delivery performance for delivery time distributions with closed forms of cumulative density functions (uniform, exponential, and logistic). Delivery performance is measured using a cost based analytical model which evaluates the expected cost for early and late delivery. The effect of changes to the parameters of the delivery time distributions on the expected cost is explored. Strategies for improving delivery performance utilizing the mean and variance of the delivery time distributions are also studied when a supplier uses an optimally positioned delivery window to minimize the expected penalty cost. Theoretical and managerial implications of the findings are discussed. Keywords: Supply Chain Management; Delivery Performance; Continuous Improvement ANALYSIS OF A NON MARKOVIAN SINGLE SERVER BATCH ARRIVAL QUEUEING SYSTEM OF COMPULSORY THREE STAGES OF SERVICES WITH FOURTH OPTIONAL STAGE SERVICE, SERVICE INTERRUPTIONS AND DETERMINISTIC SERVER VACATIONS   by Vignesh Perumal, Srinivasan S, Maragathasundari S Abstract: This paper deals with the steady state analysis of a single server batch arrival queueing system with three stages of compulsory service. An added assumption of fourth stage optional service is considered. After service completion the server may take a vacation. In this model, the vacation is of fixed duration. Busy server may break down at any instant. It is followed by a repair process. Service time, vacation time and repair time follows general distribution. The steady state probability generating function for the system is obtained by using supplementary variable method. System performance measures are also determined. Some special cases of the model are also discussed. Model is justified by means of numerical illustration. Keywords: Stages of service,Optional service,Feedback service,Deterministic Vacations,Breakdown and Repair process A case of unconstrained multiple-factor optimization with unknown function in textile industry   by Qidong Cao, Thomas Griffin, Xiaoming Li Abstract: We applied an extremal experiment in a paper machine clothing factory to solve a quality problem caused by automatic bobbin-changers. The experimental study maximized the breaking strength of weld point and therefore led to a substantial gain in the gross profit. Questions answered in the extremal experiment of this study are useful to other practitioners who can apply the extremal experiment to their industries where an unconstrained multiple-factor optimization model with unknown functions between the dependent variable and the factors is employed. Keywords: Extremal Experiment; Sequential Experiments; Steepest Ascent Method; Parameter Optimization; Factorial Design An Inventory Model for imperfect production system with Rework and shortages   by Sanjai M. Abstract: This paper considers a production inventory model with planned backorders for a single product. The product is manufactured in a single stage manufacturing system. The manufacturing system generates imperfect quality products. All these defective products are reworked in the same cycle. This paper develops two inventory models for two operational policies. The first policy covers the case that the rework is done and the shortages are not permitted. The second policy covers the case that the rework is done and the shortages are permitted. The generation of defective items during most practical production processes is almost inevitable. These imperfect quality items can sometimes be reworked and repaired, hence the overall production costs can be reduced significantly. To achieve this objective, a mathematical model is developed. In particular, the optimal production lot size which minimizes the total cost is derived. This model is developed for deriving the necessary and sufficient conditions for having a unique solution. An illustrative example is provided and validated. The validation of result in this model was coded in Microsoft Visual Basic 6.0. Keywords: EPQ, Defective Items, Cycle time, Rework, Shortages, Demand and Production. Modeling a bi-objective airport gate scheduling with controllable processing time using Hybrid NSGA-II and VNS algorithm   by Sanaz Khatibi, Morteza Khakzar Bafruei, Morteza Rahmani Abstract: While almost all studies on airport gate scheduling the processing time was considered fixed, in this research, we address a bi-objective model in more realistic situation that airport gate processing time is controllable with non-homogeneous gate. It is assumed that the possible compression/expansion processing time of a flight can be continuously controlled, i.e. it can be any number in a given interval. The aim of this study is to simultaneously (1) minimize total cost of tardiness, earliness, delay as well as compression and expansion costs of job processing time and (2) minimize the passengers overcrowding on gate. In this study, a mixed-integer programming model is proposed. For solving such bi-objective problem, two multi-objective meta-heuristic algorithms i.e. NSGA-II and hybrid NSGA-II and VNS are applied. In order to avoid the solution to be trapped in local optimum, VNS is used instead of mutation operator in NSGA-II. For calibrating the parameter of the algorithms Taguchi method is used and the optimal levels of the algorithms performance are selected. The algorithms are tested with real life data from Mehrabad International Airport for medium size problems. Computational experiments reveal that hybrid NSGA-II and VNS generate better Pareto-optimal solution as compared to NSGA-II. Keywords: Gate scheduling problem, Multi-objective decision making, NSGA-II, VNS, Hybrid meta-heuristic, Controllable processing times Analysis of Simulated Annealing Cooling Schemas for Design of Optimal Flexible Layout under Uncertain Dynamic Product Demand   by Akash Tayal, Surya Prakash Singh Abstract: Manufacturing facilities are subjected to many uncertainties such as variability in demand, queuing delays, variable task times, rejects and machine breakdown. These volatilities have a large impact on leap time, inventory cost and delivery performance of a manufacturing unit. To operate efficiently the manufacturing facilities should adapt to these variations. The paper explores the way uncertainties are addressed in designing of flexible optimal layout. Such facility layout problem is known as Stochastic Dynamic Facility Layout Problem (SDFLP). SDFLP is an NP-hard combinatorial optimization problem, which means the time taken to solve increases exponentially with problem size. To solve SDFLP, the paper presents an adaptation of Simulated Annealing (SA) meta-heuristic. Various SA cooling schemas are discussed, computed and evaluated for generating the optimal flexible layout. An optimal layout is one that minimizes the distance travelled by materials taking into account uncertain product demand (material handling cost). A computer based tool was developed and analysis was conducted on small to large size problem set. The results showed that SA with exponential cooling schedule provides better solution in terms of layout efficiency and gave better solution as compared to literature. Keywords: Facility layout; stochastic dynamic facility layout; simulated annealing; cooling schedule; meta-heuristic; modified simulated annealing An EPQ model of deteriorating substitute items under trade credit policy   by Uttam Kumar Bera Abstract: This study presents a multi item production control inventory model of deteriorating items where items are substitute in nature. Here demand is taken as stock dependent. The law of demand suggests that demand for a product is proportionally affected by its own stock. If the products stock rise demand rises or the stock falls demand falls. Moreover, demand for one good also depends on the stock of other related goods. The standard economic textbooks indicate that related products for a product include complement as well as substitute products. Two goods are substitutes (or rival) if one can be used in place of the other one. Many products that are on the market today have substitutes. For example, bread and crackers, stocks and bonds, two different brands of soft drinks or water, domestic and foreign cars, oats and corns, natural gas and electricity or two different brands of toothpaste are substitutes. The change in a substitute products stock level could alter quantity demanded for another good. When customers evaluate two substitute products for purchase, they will pay attention to characteristics of both products, including its prices and stocks, and make purchasing decisions. For that reason, customer could determine the production decisions for a product while considering its substitutes stock level in order to attain an optimal profit level. Supplier offers trade credit period to the retailer and the retailer also offers trade credit to the supplier. The whole profit is calculated with retailers point of view. In this model we take budget as crisp, fuzzy, random, fuzzy random and fuzzy rough. In different scenarios, we find the relevant profit of the retailer. The nonlinear optimization method Generalized Reduced Gradient (GRG) method and LINGO (13.0) is used to find the optimal solutions and the corresponding maximum profits for the different sets of given numerical data. Some sensitivity analyses are made and presented graphically. Keywords: EPQ model, Substitute item, Uncertain budget. Mean-Variance Investment Strategy with Proportional Transaction Costs and Withdrawal Process for a Defined Contribution Pension Scheme   by Charles Nkeki Abstract: In this paper, we consider an extension of the Markowitz portfolio and investment problem in which transaction costs are incurred; contributions and withdrawals are made by the pension plan members (PPMs) in the investment portfolio. The transaction cost are modeled as a proportion of the value of risky assets transacted. The aims of this paper are to (a) minimize the investment risks, (b) minimize the contribution risks and simultaneously maximize amount of contributions and (c) strategically minimize the amount of withdrawal by the PPMs. The optimal portfolio, contributions and withdrawal processes, with proportional transaction costs were obtained. Some numerical results are also presented in this paper. Keywords: Mean-variance, investment strategy, withdrawal, transaction costs, pension plan member, defined contribution, pension scheme Modeling the barriers of Indian Pharmaceutical Supply Chain using fuzzy AHP.   by Vinayak Vishwakarma, Chandra Prakash Garg, Mukesh Kumar Barua Abstract: The pharmaceutical and drug industry has gained attention globally as the health sector became the primary concerns. The Indian pharma industry is recognized as a rising healthcare contributor. Due to the profound discrepancy in the developing countrys production, it possibly affects the quality of producing medicine, which eventually harm the health of the patients. This attention made practitioners to come up with modern technologies and strategies to enhance a performance of the supply chain. The pharmaceutical supply chain (PSC) has a great stake in the development of sustainable strategies. Such unique supply chain is been challenged by the barriers especially in context of developing nations like India. The identification of barriers against pharmaceutical supply chain is desired to enhance the performance of the industry. This study identifies twenty eight barriers under six major criteria of Indian PSC through relevant literature and experts opinions. This paper proposes hybrid model based on fuzzy AHP to prioritize and rank barriers in PSC. This approach is also suitable in analyzing experts judgments and uncertainty involved in the process of prioritization. The findings and framework developed could be used by the drug industry, which helps them to destroy barriers to address sustainability and quality manufacturing. The analysis of the results indicates that market related barriers are the most important for Indian PSC. Further sensitivity analysis is also performed to test the robustness of the proposed approach. Keywords: Pharmaceutical Supply Chain (PSC), Barriers, fuzzy-AHP, sensitivity analysis India. An efficient hybrid meta-heuristic ant system for minimum sum coloring problem   by Kourosh Eshghi, Amin Mohammadnejd Dariani Abstract: Graph Sum Coloring Problem is a special class of graph vertex coloring problems. Because of its various applications in practical areas, especially in scheduling, many researchers have been focused on it during the past decade. In recent years, several heuristic and meta-heuristic algorithms have been developed to solve sum coloring problem.In this research, a hybrid algorithm based on mini-max ant system and simulated annealing is applied for the problem. This algorithm is tested on benchmark random graphs and compared to prior algorithms. Results show that in many cases, the best known results can be obtained or improved by the proposed algorithm. Keywords: Graph Sum Coloring, Graph Coloring, ACO Algorithm, Meta-heuristics An Integrated Production and Distribution Problem with Direct Shipment: A Case from Moroccan Bottled-Water Market   by Jamal LMARIOUH, Nizar EL HACHEMI, Mohamed Anouar JAMALI, Driss BOUAMI, Louis-Martin ROUSSEAU Abstract: We consider an industrial application encountered in the Moroccan context that involves the production and distribution of bottled water. Our industrial partner owns one plant from which all requests are delivered in direct shipments and usually full truckloads to the customers (a set of regional depots and wholesalers). We must take into account the production, the delivery requirements, multiple products, and inventory levels. The objective is to minimize the sum of the production, transportation, and inventory costs. We propose a mixed integer linear program for a variant of the multi-vehicle, multi-product production routing problem. Experiments have been conducted using CPLEX 12.3.0, and almost all instances were solved with a reasonable optimality gap. The results show that significant savings can be obtained by using our approach with respect to the current company practice. Keywords: production-distribution problem ; production planning ; vendor-managed inventory ; bottled-water A Manufacturer-Retailers Dynamic Cooperative Advertising with Retail Competition   by Peter E. Ezimadu, Chukwuma R. Nwozo Abstract: This work deals with the cooperative advertising in a manufacturer-retailers supply chain. Using Ericsons extension of Sethis sales-advertising dynamics model, it considers the manufacturer as the Stackelberg leader and the competing retailers as the followers who play a Nash game with each other. Using differential game theory it obtains a time consistent feedback Stackelberg equilibrium for the optimal advertising strategies and payoffs for all the players for a situation where retail advertising is subsidised and where it is not subsidised. It shows that while the manufacturers advertising effort reduces with the provision of subsidy, the retailers advertising efforts and the awareness shares increase. These consequently lead to increase in the payoffs for all the players. It also shows that a retailers advertising effort should be subsidised only if the rate of increase of the manufacturers payoff resulting from that retailers margin to the manufacturer is twice greater than the rate of increase of that retailers payoff. It further shows that increase in a players margin leads to increase in his payoff, and observes that while a fair player would increase his advertising effort as his profit margin increases, the other players reduce their advertising efforts, with the exception that when retail advertising is subsidised, a retailer increases his advertising effort as his margin to the manufacturer gets larger. Keywords: Supply chain, Stackelberg differential game, Nash differential game, Sethi’s sales-advertising model, Ericson’s extension of Sethi model Customers Order Acceptance and Scheduling to Maximize Total Profit   by Mehdi Fazeli-Kebria, Ghasem Moslehi, Naser Mollaverdi, Mohammad Reisi-Nafchi Abstract: In this paper, in order to maximize total profit the order acceptance and scheduling problem was generalized by considering some customers with their own orders, who do not agree with partial rejection/acceptance of them. Therefore, it was assumed that accepting or rejecting one customer is equal to accepting or rejecting all his orders. In addition, the considered penalty function for scheduling the orders was total weighted lateness. A mathematical programming model, an upper bound, a branch and bound, and an efficient heuristic algorithm were proposed for this problem. It was shown that before starting the problem solving procedure, it is possible to certainly reject or accept some customers. The proposed branch and bound algorithm solved 93% of 810 randomly designed problem instances in a reasonable time. Besides, the heuristic algorithm solved the problem instances with the size of 2000 customers at most with 0.1% deviation from a lower bound. Keywords: Scheduling; customer; order acceptance; lateness; branch and bound Linear Fractional Programming Problems With Some Multi-choice Parameters   by Mahendra Biswal, Avik Pradhan Abstract: Linear fractional programming is a class of mathematical programming problem where we optimize the ratio of two linear functions subject to some linear constraints. In this paper, we present a linear fractional programming model where some or all the parameters are multi-choice type. We present a novel and ecient method, which integrates classical Charnes-Cooper transformation and\r\nLagrange\'s interpolating polynomial, to transform multi-choice linear fractional programming problems into an equivalent mixed-integer nonlinear programming (MINLP) problems. A theorem is presented to establish the relation between the optimal solution of the multi-choice linear fractional programs and the equivalent MINLP. Some numerical examples are studied to illustrate the methodology. Keywords: Linear fractional programming; Multi-choice programming; Transportation problem; Interpolating polynomial; Mixed integer programming. Preemptive Just-in-time scheduling problem on uniform parallel machines with time-dependent learning effect and release dates   by Keyvan Shokoufi, Javad Rezaeian, Babak Shirazi, Iraj Mahdavi Abstract: This paper considers uniform parallel machines scheduling problem with time-dependent learning effects, release dates, allowable preemption and machine idle time to minimise the total weighted earliness and tardiness penalties which is known to be strongly NP-hard. To solve this problem, this research proposes a Mixed Integer Non-Linear Programming (MINLP) model. Afterward, in order to find the best solution in an effective solution space, a dominant set is proposed for the length of the schedule experimentally. Also, based on the allowable idle time, a new time-dependent learning model on parallel machines is proposed. Furthermore, a genetic algorithm (GA) and a hybrid of genetic algorithm and particle swarm optimisation (HGA-PSO) are proposed. Taguchi method is applied to calibrate the parameters of the proposed algorithms. Finally, the computational results are provided to compare the results of the algorithms. Then, the efficiency of the proposed algorithms is discussed. Keywords: Just-in-time scheduling; Uniform parallel machines; Time-dependent learning effect; Preemption; Machine idle time; Release date. Net present value maximization of integrated project scheduling and material procurement planning   by Babak H. Tabrizi, Seyed Farid Ghaderi, Siamak Haji-Yakhchali Abstract: A mixed-integer programming model is developed in this paper to consider simultaneous planning of project scheduling and material procurement problem as an efficient approach to improve project costs. The proposed formulation provides the model with the possibility to procure materials from a number of suppliers each offering a distinctive all-unit discount method. The purpose of the mathematical model is to develop schedules with the best net present value in order to guarantee successful completion of the project. We have applied a genetic algorithm to solve the problem, whose key factors are tuned by the Taguchi method. It is discussed how to generate initial feasible solutions as a preprocessing method to start the solution algorithm. Finally, the efficiency and applicability of the model is tested by a different set of category-based instances. Keywords: Project scheduling; Procurement; Net present value; Genetic algorithm. Order Acceptance and Scheduling: Overview and Complexity Results   by Venkata Prasad Palakiti, Usha Mohan, Viswanath Kumar Ganesan Abstract: We provide an overview of existing Order Acceptance and Scheduling (OAS) problems by considering due date related dimensions in deterministic scenarios. We introduce a three field notation for classifying the OAS problems and we review solution algorithms and complexity results for the same. We prove the complexity results for existing, open as well as new OAS problems with due dates; and provide the complexity results for newly defined OAS problems without due date dimensions. Keywords: Complexity; Order acceptance; Scheduling; Due dates. Multi-Objective Fuzzy Probabilistic Quadratic Programming Problem   by Prabhat Rout, Sudarsan Nanda, Srikumar Acharya Abstract: The aim of the paper is to present a multi-choice multi-objective fuzzy probabilistic quadratic programming problem and its solution methodology. The mathematical programming problem suggested here is difficult to solve directly. Therefore, three major steps are suggested to solve the proposed mathematical programming problem. In first step, fuzzy chance constraint is transformed to its equivalent chance constraint programming problem using \alpha-cut technique. Chance constraint technique is used to obtain a crisp multi-choice multi-objective quadratic programming problem. In next step, importance is given to handle multi-choice parameter using least square approximation technique. At the end of second step, a multi-objective quadratic mathematical programming is obtained. Finally, goal programming approach is used to solve the transformed multi-objective quadratic mathematical programming. Using existing methodology and software the final solution of the proposed model is obtained. The proposed method is implemented with a numerical example. Keywords: Multi-objective, Fuzzy probability, Goal programming, Multi-choice programming, Least square approximation. A New Local Search Heuristic based on Nearest Insertion into the Convex Hull for Solving Euclidian TSP   by Mir Mohammad Alipour Abstract: The Traveling Salesman Problem (TSP) is probably the most famous and extensively studied problem in the field of combinatorial optimization. This problem is in the fields of logistics, transportation, and distribution. Since the TSP is NP-hard, many heuristics for the TSP have been developed. In this paper, we developed a novel local search heuristic, based on Nearest Insertion into the Convex Hull construction heuristic for solving Euclidian TSP. The proposed method, Nearest Insertion into the Convex Hull Local Search (NICH-LS) is used to improve the initial tour, which is taken from a tour construction heuristic, Multiagent Reinforcement Learning (MARL) heuristic, by locally manipulating the order of nodes in the consecutive partial tours of the initial tour. Changing the order of nodes in a partial tour are done via constructing the NICH tour of these nodes and replacing the partial tour with the modified partial tour, if its length is reduced. The proposed novel local search heuristic is applied to 29 benchmark instances from TSPLIB. The computational results show the efficiency of the proposed local search compared with five state-of-the-art heuristics. Keywords: Local Search; NICH-LS; Traveling Salesman Problem; MARL; Nearest Insertion; Convex Hull A new approach for integrated surgical procedure scheduling with arrival uncertainty   by Asie Soudi, Mehdi Heydari Abstract: Efficient utilization of an Operating room is a common anxiety of surgical suit managers, which necessitate an effective planning and scheduling of surgeries. In this paper we develop a new version of hybrid flow shop scheduling for weekly planning and scheduling of an integrated operating theatre with multi operating rooms by considering the capacity of beds in a ward. Then, a converting technique is applied to change the non-linear model into a linear one by introducing new variables and constraints. Our goal is to suggest an integrated way of encountering operating theatre scheduling to coordinate hospitalisation and the time of surgery, in terms of minimising makespan and the waiting time of patients based on the problems reported by a typical non-profit hospital in Iran. The proposed model is compared with the current approach in the case study. By further considering the uncertainty, resulted from the arrival of emergency patients, a new approach is proposed, which contains a chance constrained programming model for predictive phase and a new reactive programming model for a reactive phase. We show how applying a chance constrained programming method to deal with arrival uncertainty will reduce by considering a virtual patient in hospital. The efficiency of the proposed approach is demonstrated through computational results in comparison with classic one. Keywords: stability of primary schedule; integrated surgical procedure; chance constrained programming; emergency patient; sequencing GOAL PROGRAMMING MODEL FOR PREDICTING THE PARAMETERS INVOLVED IN GROWTH OF CANCER CELLS   by Jayabharathiraj Jayabalan Abstract: Some non-linear goal programming problems are formulated for predicting the decision parameters involved in the cancer and related cell growths such growth rate of normal & malignant cells, death rate of normal & malignant cells and mutation rate of normal cells. An application of preemptive and non-preemptive goal programming problem, some optimization programming problems are constructed using the statistical moments derived from two state cancer cell growths model and solved using the optimization LINGO software. Keywords: Goal Programming Problem (GPP), Birth and Death Processes, Cancer Cell Growths. Structural modelling and analysis of Production System Life Cycle (PSLC): A Graph Theoretic Approach   by Rajesh Attri, Sandeep Grover Abstract: The main purpose of this paper is to cultivate a cohesive system model for the structural modelling and analysis of Production System Life Cycle (PSLC) in terms of its prompting systems and interfaces between the systems and sub-systems. By means of Graph Theoretic Approach (GTA), PSLC is first modelled with help of graph theory, then by matrix method and at last, by a multinomial known as a permanent function. Different factors (hereby known as quality enabled factors) affecting the PSLC or its decisions are identified to develop a graph theoretic model, a matrix model, and a multinomial permanent model of the PSLC. The present work recommends an index with the help of GTA which can be effectively employed for assessing the quality of decisions made in the different phases of PSLC. The step by step procedure for the application of GTA methodology is itemized with an example that may assist the managers or decision makers to implement it in their organizations. Keywords: PSLC; GTA; Index; methodology; decision; QEFs. Modeling Trends in Road Crash Frequency in Qatar State   by Galal Abdella, Khalifa Al-Khalifa, Maha Tayeseer, Abdelmagid Hamouda Abstract: The data based regression models are widely popular in modeling the relationship between the crash frequencies and contributing factors. However, one common problem usually associated with the classical regression models is the multicollinearity, which leads to biased estimation of the model coefficients. This paper mainly focuses on the consequences of multicollinearity and introduces a multiple objective based best-subset approach for promoting the accuracy of the road crash model in Qatar-State. The prediction performance of the methodology is verified through a comparative study with two of well-known time series models, namely Autoregressive Moving Average (ARMA) and Double Exponential Smoothing (DES). The Mean Absolute Percentage Error (MAPE) is used to assess the ability of each model in maintaining minimum prediction errors. The methodology is illustrated by using a data set of road crashes in Qatar-State, 2007-2013. Keywords: ARMA; multicollinearity; road crash modeling A heuristics approach for computing the largest eigenvalue of a pairwise comparison matrix   by Nachiappan (Nachi) Subramanian, Ramakrishnan Ramanathan Abstract: Pairwise comparison matrices (PCMs) are widely used to capture subjective human judgements, especially in the context of the Analytic Hierarchy Process (AHP). Several methods are available to estimate priorities of elements from a PCM, the Eigenvector Method (EM) being the most common. Since the PCMs involve the use of human judgements, procedures to check the consistency of judgements is considered an important requirement while computing the priorities, as the priorities estimated from highly inconsistent judgements seem to be unreliable for further use. Consistency of judgements is normally computed in AHP context in the form of consistency ratio (CR), which requires estimation of the largest eigenvalue (max) of PCMs. Since max is automatically computed in the EM, computing CR is not a serious issue when EM is used to estimate priorities. However, EM is not the only method for estimating priorities from PCMs. Several alternatives to EM have been reported in the literature. Since many of these alternative methods do not require calculation of eigenvector, max and hence the CR of a PCM cannot be easily estimated. We propose in this paper a simple heuristics for calculating max without any need to use EM. Simulation is used to compare the accuracy of the proposed heuristics procedure with actual max for PCMs of various sizes. It has been found that the proposed heuristics is highly accurate, with errors less than 1%. The advantage of the proposed heuristics is that it can be easily calculated with simple calculations without any need for specialised mathematical procedures or software and is independent of the method used to derive priorities from PCMs. Keywords: Multiple Criteria Analysis, Pairwise Comparison Matrix, Eigenvector Method, the Largest Eigenvalue, Consistency index. An integrated model of cell formation and scheduling problem in a cellular manufacturing system considering automated guided vehicles' movements   by Saeed Dehnavi-Arani, Mohammad Saidi-Mehrabad, Vahidreza Ghezavati Abstract: In this paper, an integrated mathematical model for Cellular Manufacturing System (CMS) incorporating Cell Formation Problem (CFP) and intra-cell scheduling is considered. It is assumed that in order to make the flexibility in handling system, Automated Guided Vehicles (AGVs) are responsible for transferring the exceptional parts from one cell to other cell. Employing the AGVs in CMS can be challenging from mathematical model point of view. In other word, despite the common constraints in CFP and intra-cell scheduling, several constraints such as AGVs movement, preventing the AGVs collision as well as parts pickup/delivery by AGVs must be taken to account. There is not a comprehensive model including role of AGVs in a CMS. Hence, we make endeavor to formulate CFP, scheduling and role of AGVs at the same time. The objective function is to minimize the sum of the maximum completion time (makespan) and inter-cellular movements of parts. The proposed nonlinear model is transformed to a linear form in order to solve it for optimality. Eventually, two small-size computational experiments are generated and ran on the GAMS.9 commercial software to show the efficiency and accuracy of the proposed model. Keywords: Cellular manufacturing system, Cell formation problem, intra-cell scheduling, inter-cellular AGV Fuzzy AHP Model for Challenges to Thermal Power Plant Establishment in India   by Varinder Kumar Mittal, Rahul Sindhwani, Himanshu Shekhar, Punj Lata Singh Abstract: Thermal power plant is a huge project in context of resource utilization, time required for its completion and also large amount of funds are required for its establishment. So, delay in any activity especially activity of critical path would results in delay of whole project. Due to delay, the stress on the resources would also increase, which may further results in substandard quality of work. A fuzzy AHP process model has been used to prioritize the challenges to thermal power plant establishment through the inputs by experts from managers at the actual construction sites, designers, and consultants. The challenges identified in the present study can be categorized in three categories viz. high impact, medium impact and least impact challenges. This paper presented the ranking of 19 challenges faced in establishment of a new thermal power plant in India. Keywords: Fuzzy AHP; Challenges; Project Management; Ranking; Multi-Criteria Decision Making; Thermal Power Plant; Project Management Analyzing of a finite buffer queue with finite number of vacation policy and correlated arrivals   by Karabi Sikdar Abstract: This paper analyzes a MAP/G/1/N queue having finite number of vacations. The server takes a finite number (say J >= 0) of vacations whenever the system becomes empty at service completion epoch. If no clients are found by the end of the J^{th} vacation, the server does not go for vacation and stays in the system (called dormant period) until one client arrives. The number of vacation being finite, the server can utilize vacation period for any other jobs. This is obvious when J=1 and J rightarrow infty lead to single and multiple vacation models respectively. This research work mainly focuses more generalized vacation policy and different use cases. The following results have been obtained: (i) the distributions of clients in the queue at various epochs (ii) the Laplace-Stieltjes transform of the actual waiting-time distribution in the queue of a client under the FCFS discipline. The numerical data and graphs are presented to establish the analytical result. Keywords: Finite buffer, Finite vacation, Markovian arrival process, Queue, Steady state, Single server, Waiting time Application of Fuzzy ANP method to select the best supplier in the supply chain   by Habibollah Danai, Shahram Hashemnia, Rokhshad Ahmadi, Seyed Hojjat Bazazzadeh Abstract: Positive performance of purchase department has a direct impact on reducing cost and increasing profitability,and survival of supply chain. One of the major tasks of purchase department in the supply chain is to evaluate and select suppliers. The process of selecting a suitable supplier among different options and variables is an important task.Inappropriate selection of supplier in addition to impose more costs will have devastating impacts on the organization performance.The main objective of this study is to provide a useful approach to fuzzy ANP for evaluation of issues related to supplier selection.Many quantitative and qualitative concerns may be brought by the issues related to supplier selection,they are complicated issues. In this study, an ANP model was designed in fuzzy environment; through which the best accessories suppliers in Hiva Sanat Company are identified and prioritized. Keywords: Fuzzy ANP, supply chain Novel criterion models in the inverse DEA problem   by Mojtaba Ghiyasi Abstract: This article deals with "inverse" Data envelopment analysis (DEA) problem which is a mathematical programming based technique. The process of checking perturbed DMUs is simplified by proposing a new criterion model. This yields to a reduction of computational complexity for the criterion model. In addition, more realistic criterion model is also proposed and the relationship between existing criterion model and proposed models are discussed. Moreover, it is shown that proposed models solve some problematic failures of the existing inverse DEA models in the literature. Two numerical examples are provided to illustrate the idea. Proposed model are illustrated by a real life data and a comparison between existing criterion model in the literature and proposed criterion models is also provided. Keywords: Inverse DEA; Input/output estimation; Criterion model; Multiple-objective programming. An analysis of discrete time Geo/Geo/1 queue with feedback, repair and disaster   by Sebasthi Priya, Sudhesh R Abstract: A discrete-time queue with feedback subject to system disaster, server failures and repair is considered in this paper. The time-dependent system-size probabilities are obtained by using generating functions where the system of difference equations in two parameters namely time epoch(m) and number of customers(n) are transformed into another difference equations in terms of generating functions. The difference equations of generating functions satisfy a three-term recurrence relation which leads to continued fraction. System-size probabilities and some performance measures in steady-state are derived. Further, busy period distribution, reliability and availability are also obtained. Numerical illustrations are provided for different parameter values to see their effect on performance measures and to get more insight of the model behavior. Keywords: Disasters - feedback - reliability - busy period -continued fractions. Optimal Bargaining Mechanisms with Refusal Cost   by K. R. Ramkishore, R K Amit Abstract: We consider a bargaining situation between an arriving buyerrnand a seller, where the buyer and the seller have private valuations.rnThe seller has an inventory, which has to be sold over the infiniternhorizon. It is assumed that the seller incurs a refusal cost, if the traderndoes not take place. Myerson (1985) discusses four bilateral bargainingrnmechanisms in static settingsprice negotiation, splitting the differencernbetween sellers and buyers offer, buyer posted price and seller postedrnprice. The objective of this research is to study these mechanisms in therndynamic setting, with consideration of the refusal cost. In this paper, wernmodel the situation as a Markov decision process, which endogenizesrnthe sellers marginal inventory valuation. We find that the seller prefersrnposting prices when the refusal cost is low. Seller is indifferent betweenrnbuyer posted price and negotiation for the high refusal cost. Keywords: Markov Decision Process; Bilateral Bargaining Mechanisms; Refusal Cost A Pruned Pareto Set for Multi-Objective Optimization Problems Via Particle Swarm and Simulated Annealing   by Ahmad Abubaker, Adam Baharum, Mahmoud Alrefaei Abstract: A Pareto optimal set, which is obtained from solving multi-objective optimization problems, usually contain a large number of optimal solutions. This situation poses a challenge for decision makers in choosing a suitable solution from a large number of overlapping and complex Pareto solutions. This paper proposes a new procedure for solving multi-objective optimization problems by reducing the size of the Pareto set. The procedure is divided into two major stages. In the first stage, the multi-objective simulated annealing algorithm is used to solve a multi-objective optimization problem by constructing the Pareto optimal set. In the second stage, the automatic clustering algorithm is used to prune the Pareto set. This procedure is implemented to solve two multi-objective optimization problems, namely, the 0/1 multi-objective multi-dimensional knapsack problem and the multi-objective inventory system. The procedure enables the decision maker to select an appropriate solution efficiently. Keywords: Multi-Objective Problem; Inventory Control; Simulated Annealing; Particle Swarm Optimization; Automatic Clustering. Wise Intrusion Detection System using Fuzzy Rough Set based Feature Extraction and Classification Algorithms   by Selvakumar Kamalanathan, SaiRamesh Lakshmanan, Kannan Arputharaj Abstract: In recent times, it is critical to give abnormal state security to guarantee protected and successful correspondence of data through the Web. Nonetheless, secured information correspondence over the Internet or some other system is dependably a tested undertaking because of the risk of interruptions and assaults. Along these lines, Intrusion Detection Systems (IDS) have turned into a key segment in system security. Previously, different methodologies were used for creating interruption in location frameworks. In any case, sadly, any of these frameworks are not totally faultless because of the vulnerability of system activity made by ordinary clients and assailants. Henceforth, the requirement for the advancement of productive IDS has expanded consistently. This work proposes a versatile IDS taking into account Fuzzy Rough sets for characteristic determination. Also, another fluffy unpleasant set based nearest neighbourhood grouping is proposed for powerful arrangement of the KDD container dataset. This model uses a biased dataset that has 50:50 Normal and Attack information rather than the ordinary datasets that have 80:20 Normal and Attack information. The effectiveness of the proposed IDS is upgraded because of the utilization of one-sided information. The blend of highlight determination and characterization utilizing biased information set diminishes the false alert rate and builds the identification precision. Keywords: Intrusion Detection System; Intrusion detection; FRNN; Fuzzy rough set; Nearest Neighbourhood; Biased dataset Parametric Multi-objective Fractional Programming Problem with Interval Uncertainty   by Ajay Bhurjee, Geetanjali Panda Abstract: The present work defines interval and interval valued function in terms of parameters. Two types of multi-objective programming problems are considered: one is a general multi-objective interval fractional programming problem and the other is a parametric form of the first problem, where the objective and constraint functions are interval valued. Relationship between the solutions of both problems are developed, and some of the results are illustrated through numerical example. Keywords: Interval valued function; Multi-objective programming problem; Fractionalrnprogramming problem; Efficient solution. A Dynamic Programming Model For Perishable Inventory Management   by Dipankar Mandal, Sri Vanamalla Venkataraman Abstract: The amount of inventory should neither be in excess nor be inadequate. Excess inventory will result in additional cost to the company and lack of sufficient inventory will result in loss of customer demand. Thus in order to maintain an optimum inventory, inventory management is required. Inventories can be broadly classified as: Perishable and non-perishable inventories. Perishable inventories have a limited lifetime and hence it may happen that a substantial quantity of such products get outdated and hence wasted. Along with this loss an additional cost due to outdating may be incurred. In this paper we propose to increase the overall profit by reducing such costs; we classify the entire lifetime of such products into two periods: in the first period a customer derives a higher utility from the product and during the second period a customer derives a lower utility from the product. In traditional models discussed in literature the net profit which is the difference between selling price and overall costs, is being maximized. Through this research we propose a modification of this traditional model by varying the preferences of the product and hence its price over time. Under assumptions of stochastic demand we compare the traditional model with the modified model through numerical simulations. Our results indicate an improvement over the traditional model. Keywords: inventory management; perishable products; dynamic programming Side constrained traffic assignment problem for multiclass flow   by Saeed Asadi Bagloee, Mohsen Asadi Abstract: Despite many realistic features represented by capacity constraints in traffic assignment (generally known as side constraints), they are largely overlooked by both practitioners and scholars due to the inherent mathematical complexities. Such complexity is heightened in the context of multiclass traffic flow. To overcome such complexities; we first relaxed the capacity constraints by an intuitive interpretation of the corresponding Lagrange values, that is, the amount of penalty imposed to the travel time of the oversaturated road to make them saturated. This approach is basically a subgradient method and is dubbed inflated travel time (ITT). The penalty terms bear some resemblances to the marginal cost of the concept of system optimal traffic flow and congestion pricing. We then circumvented the multiclass facet by adopting a bias term for each user class in the Beckmann objective function. Hence the capacitated multiclass Traffic Assignment Problem (TAP) becomes an uncapacitated single-class TAP in which the aforementioned additional penalty is updated iteratively. The benchmark network of Hearn and the network of the city of Winnipeg are used for numerical evaluations. This study contributes to the literature by providing a new method free of any additional parameter. In the past studies, parameters calibrations are not trivial tasks. Furthermore, this study addresses the much awaited needs in the industry, by casting the ITT as an easy-to-use module in EMME 3 a leading commercial software- at a click away. Keywords: Multiclass Traffic Assignment; Side constraint; Capacity constraint Cross Trained Servers with Balking and Feedback Service Facility by applying Constraint Programming Model   by Poongothai Venugopal, Godhandaraman P Abstract: This paper deals with a service facility which has front and back room operations. In the front room, the servers deals with serving customers, perhaps from a queue and those in the back room perform a job which is generated by the front room. In service facilities, two major issues have been considered. A constraint programming model is used to solve queue control problem. rnA customer on arrival finds other customers in the front room, may join the queue or may leave service facility. After completing the front room service if the customer is unsatisfied with the service, he may rejoin for service again until the service is completed successfully. The goal is to determine the expected waiting time in the queue subject to back room constraints. The effects of various parameters on the performance measures are analyzed numerically. Keywords: Switching time, Balking, Feedback, Constraint programming, Optimization. Forecasting of quay line activity with neural networks   by Iñigo L. Ansorena Abstract: This paper presents a GRNN (Generalized Regression Neural Network) to forecast the activity of the North Quay at the port of Callao (Peru). To the author's knowledge, this is the first application of Artificial Neural Network theory to container terminals in South America. On the basis of service characteristics, operating profiles, and dimension of vessels, the model examines the berthing line. Five numerical variables are used to estimate one dependent variable. The results achieved are satisfactory and the model built up using Neural Network theory is able to estimate the staying time of vessels in Port. Keywords: neural network, berthing line, Callao port Inventory model with Quantity discount, pricing and partial backlogging for a deteriorating items   by Rakesh Tripathi, Dinesh Singh Abstract: Inventory model with quantity discount, pricing and partial backordering, although common in practice, have received very little attention from researchers. The objective of this study is to develop a deterministic inventory model with quantity discount, pricing and partial backordering when the product in stock deteriorates with time. Most retailers make pricing decisions of their products at certain times of the year and these decisions affect demand. In this study a power law form of the price dependence of demand is considered. The rate of deterioration is taken to be linear function of time. Solution procedure is provided for finding numerical results. The model is solved analytically. A numerical example is given to illustrate the theory. Sensitivity analysis is given to validate the proposed model. Keywords: Inventory; shortage; pricing, partially backlogged, deterioration A DEA-PROMETHEE approach for complete ranking of units   by Maryam Bagherikahvarin Abstract: Data Envelopment Analysis (DEA) and Multiple Criteria Decision Aid (MCDA) are two well-known approaches to rank so-called Decision Making Units (DMUs) or alternatives. In this contribution, a two-step model is presented to completely rank units according to multiple inputs and outputs. In the first step, DEA is applied between each pair of DMUs independently to generate a pairwise comparison matrix. In the second step, the obtained matrix is exploited by means of PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) to completely rank units. We show the compatibility between the resulting ranking of DEA and DEA-PROMETHEE methods while there exist just one input and one output. We also discuss the monotonicity property of the method. We compare DEA-PROMETHEE with an integrated DEA-AHP approach on a numerical example. Keywords: Data Envelopment Analysis (DEA); Multiple Criteria Decision Aid (MCDA); PROMETHEE; Efficiency; Ranking; Decision Making (DM) Preferable Pareto optimal solutions for specified key objective functions to multiple objective linear programming problems using trade-off ratios under fuzzy environment: An iterative process   by Arindam Garai, Tapan Kumar Roy Abstract: In this paper, one general iterative process is proposed for obtaining preferable Pareto optimal solutions, based on specified key objective functions, to multiple objective linear programming problems under fuzzy environment. In reality, decision maker usually specifies one key objective function to each of such problems. But there are known disadvantages in applying existing fuzzy optimization techniques, in which weights, utility functions etc. are used; whereas in other techniques, none of the objective functions can be specified effectively as key objective function. Moreover, correlation between key objective function and other objective functions may not be exactly known to the decision maker. In existing interactive fuzzy optimization techniques, initially developed by Sakawa et al (1984), all such reference levels of fuzzy objective functions are taken as unity. But we may find it unrealistic to expect each of conflicting objective functions to attain individual goals simultaneously. In this paper, we propose to employ trade-off ratios of membership functions of fuzzy objective functions to determine corresponding reference membership levels analytically and develop one iterative process to find preferable Pareto optimal solutions under fuzzy environment. Numerical examples further illustrate our proposed iterative process. Finally conclusions are drawn. Keywords: Fuzzy decision making; Fuzzy optimization; Iterative process; Key objective functions; Multiple objective linear programming; Pareto optimal solutions; Reference membership levels; Trade-off ratios. Optimal manoeuvre for two cooperative military elements under uncertain enemy threat   by Dušan Hrabec, Jan Mazal, Petr Stodola Abstract: Consider an armed military group of two friendly elements that need to move between two given locations - a starting point and an end point - in an environment with the possible presence of armed enemy elements. The potential positions of the enemy may or may not be known in advance. Therefore, we capture the possible threat by identifying the locations that are advantageous from the enemy's perspective. We model the problem using a stochastic programming approach. We then provide a deterministic reformulation of the problem in consideration of this possible threat and, through the use of cooperation, we minimise the total predicted threat as well as the suitability of the terrain for movement (or total length). Finally, we provide concrete experimental calculations and visualisations in our tactical information system, which serves as a tool for commanders to support their decision-making processes, and compare the obtained results to a selection of different approaches. Keywords: Optimal/safe manoeuvre; operations research; mathematical modeling; decision-making; stochastic optimisation; shortest path; military tactics; cooperative military elements. Flexible target setting in mergers using inverse data envelopment analysis   by Gholamreza Amin, Amar Oukil Abstract: In a merger, one important issue is the estimation of the levels of inputs and outputs required from each merging decision making unit (DMU) so that the merged entity can realize a desired efficiency target. This paper uses the potential of inverse data envelopment analysis (InvDEA) to build a flexible target setting of the inputs and outputs. This study expands the application of the InvDEA methodology in a merger by introducing a flexible target setting that allows the decision maker to favour specific input in the target setting. We use a dataset of thirty universities to illustrate the practical scope of the proposed flexible target setting method, which can obviously be employed in any other merging context. Keywords: Data envelopment analysis; Inverse data envelopment analysis; Mergers; Multiple optimal solutions; Flexible target setting. Multi-item Multi-choice Integrated Optimization in Inventory Transportation Problem with Stochastic Supply   by Gurupada Maity, Sankar Kumar Roy Abstract: This paper explores the study of multi-item multi-choice transportation problem (TP) in the ground of inventory optimization.Using the concept of basic inventory optimization, we develop a methodology for integrated optimization in inventory transportation (IOIT) to reduce the logistic cost of a system. To accommodate the present situations of real-life TP, the stochastic supply is taken into consideration in the article. We describe a technique to reduce stochastic constraint to deterministic constraint with the help of stochastic programming. An algorithm is presented to solve the proposed problem using MATLAB. Then the proposed problem is solved by well known optimization technique; and the obtained solution is compared with the solution of basic inventory optimization method. An example is presented to verify the effectiveness of the paper. Keywords: Transportation Problem; Multi-Item Inventory Problem; Multi-choice Programming,Stochastic Programming. The consideration of opened facilities operational volumes in designing supply chain network   by Vo Hung Duong, Nguyen Hung Bui Abstract: In this paper, we deal with a single-item, multi-period, two-echelon capacitated facility location problems where manufacturing plants and distribution centers (DCs) are decided to be opened or not at the pre-determined potential sites. At each opened facility, our model controls operational level over or under its minimum requirement volume. To do that, we use the big M technique to detect running status. If the opened facility runs at lower minimum requirement volume, penalty cost will occur and add to objective value, which should be as low as possible. This information helps the investors and managers to evaluate performance of their supply chain (SC) network system. The problem is formulated as a mixed integer linear programming (MILP) model with the objective to minimize the total cost including transportation costs, production costs, inventory holding costs, fixed costs for opening facilities, and penalty costs. Based on the specific structure of the developed model, we need one additional constraint set before using Lagrangian relaxation algorithm to solve the problem. Numerical experiments are then conducted to compare the solution of the proposed approach as opposing to the optimal solution obtained by the commercial Lingo solver. Keywords: supply chain; mixed integer linear programming; lagrangian relaxation; network design. Minimizing Total Weighted Tardiness with Considering Compulsory Idle Times on Single Machine   by Hossein Zoulfaghari, Javad Nematian Abstract: In this paper, we introduce a total weighted tardiness minimization problem of single machine system with considering compulsory idle times of machine (such as maintenance operations, etc.). Then, the problem is solved by using a variable restricted neighborhood search approach. In this approach, an algorithm has been designed in which some special methods are used to produce and improve good initial solution. To represent our algorithm performance, 200 instances with small and medium sizes and 200 instances with large sizes are solved and their results have been achieved in favor of minimization of weighted total tardiness. Furthermore, special relations operated based on a factor are used to produce instances and obtained results are investigated. Keywords: Total Weighted Tardiness, Single Machine, Compulsory Idle Time, Variable Restricted Neighbourhood Search Survivable multi-commodity network flow design; Case of Node Capacities and arc failure   by Majid Anisi, Hasan Salehi Fathabadi Abstract: This paper is focused on the design of a survivable network with node capacitiesrnand flow restrictions. The goal is to design a survivable network at minimumrncost so that feasible flows exist at the time of multiple simultaneous failurernarcs (failure scenario). To solve this problem Benders decomposition (BD) wasrninitially proposed and, then, a new strategy was presented to obtain specificrnfailure scenarios.We computationally demonstrated that Benders decompositionrnusing this strategy could obtain the optimal solution faster. This strategy reduced iterations more greatly than the BD approach. Using this strategy, the length of CPU time required for solving the problem was reduced by 30%on average. Keywords: Survivable; Network Design; Benders’ Decomposition; node capacities. A two-stage method for the Capacitated Multi-Facility Location-Allocation Problem   by Martino Luis, Chandra Ade Irawan, Arif Imran Abstract: This paper examines the capacitated planar multi-facility location-allocation problem, where the number of facilities to be located is specified and each of which has a capacity constraint. A two-stage method is put forward to deal with the problem where in the first stage a technique that discretises continuous space into discrete cells is used to generate a relatively good initial facility configurations. In Stage Two, a Variable Neighbourhood Search (VNS) is implemented to improve the quality of solution obtained by the previous stage. The performance of the proposed method is evaluated using benchmark data sets from the literature. The numerical experiments show that the proposed method yields competitive results when compared to the best known results from the literature. In addition, some future research avenues are also suggested. Keywords: capacitated; continous location problem; heuristics; variable neighbourhood search. Mean Response Time of a Two Stage Open Queueing Network Model with Feedback   by Suresh Pathare, Vinayak Gedam Abstract: The response time plays an important role in studying the various characteristics of queueing network models with feedback. Data based recurrence relation is used to compute a sequence of response time of queueing network models with feedback. The sample means from those response times, denoted by $h r_1^F$ and $h r_2^F$ are used to estimate true mean response times $r_1^F$ and $r_2^F$. Further we construct some confidence intervals for response times $r_1^F$ and $r_2^F.$ We investigate the accuracy of the different confidence intervals of $r_1^F$ and $r_2^F$ and the proposed estimators $h r_1^F$ and $h r_2^F$ by calculating the coverage percentage, average length, relative coverage and relative average length with the help of numerical simulation study. Keywords: Response Time; Coverage percentage; Relative coverage; Relative average length; Feedback; Confidence Intervals. One-for-One Period Policy and its Optimal Solution over a Finite Horizon   by Mohammadbagher Afshar-Bakeshloo, Fariborz Jolai, Mostafa Mazinani, Farhad Salehian Abstract: Recently, a new ordering policy named one-for-one period policy has been introduced in a steady state condition for the zero ordering cost with an assumption of lost sales. In this policy, constant time interval between two consecutive unique orders is assumed. In contrast to this policy, this paper addresses a new approach in which inter-arrival times are determined in a finite horizon with limited amount of arrivals. Due to the transient condition of our approach, namely (S(n),1), matrix multiplication must be employed, but it quickly becomes cumbersome for large n as there are (n- 1) decision variables for n arrivals. Thus, we have invoked the genetic search strategy to reduce the amount of search. Finally, we provide a numerical analysis to evaluate the performance of our approach. The results showed that by applying the suggested approach we can save cost compared with the one-for-one period policy for n<200, especially when the ratio of lost sales to holding cost is large. Furthermore, arrivals scheduling creates a dome-shaped inter-arrival times. Keywords: Inventory control; one-for-one period policy; scheduling; poisson process; Genetic Algorithm. The Effect of Market Concentration on Total Welfare and its Distribution in a Supply Chain Case   by Tchai Tavor, Tchai Tavor, Uriel Spiegel Abstract: The concentration of industries affects the total welfare of the economy and changes its distribution among all economic agents. This paper examines an entire supply chain that includes multiple stages within the cheese industry. It presents the effect of market concentration levels on prices, quantities, profits, consumer surplus and the total welfare of all sectors involved in the production and consumption processes. Since increased concentration may lead to a higher degree of inequality, the relationship between inequality and efficiency is investigated. These issues are demonstrated by using a simplified structure of the cheese industry. Keywords: Concentration; Herfindahl-Hirschman index; supply chain; monopoly and retailers. Modeling Sustainable Procurement Problem: A Goal Based Approach   by Harpreet Kaur, Surya Prakah Singh Abstract: Sustainable procurement problem not only considers traditional parameters such as purchasing cost, ordering cost, holding cost, and logistics cost but also considers non-traditional parameters such as carbon emission cost, thus, making it a multi-objective optimization problem. However, in competitive business scenario, organizations have also some set targets for various traditional and non-traditional parameters making the procurement process a goal oriented one. These goals in turn make the overall procurement problem conflicting while the organization tries to achieve all these goals with minimum deviations. Therefore, this paper is a novel attempt to optimize Multi-objective Sustainable Procurement Problem (MoSPP) in presence of various goals using goal programming approach and is referred as Multi_Goal_SPP. The goals for goal programming formulation are computed through MoSPP. The methodology is demonstrated through two illustrative examples solved in LINGO 10. Multi_Goal_SPP establishes optimal tradeoff between conflicting goals set by organization. The proposed model ensures the minimum total deviation from goals required to meet specified demand. Keywords: Multi-objective Sustainable Procurement Problem (MoSPP), Multi_Goal_SPP, Sustainable procurement problem, Goal programming, Multiple objectives, Carbon emissions. A Genetic Algorithm for a Flow Shop Scheduling Problem with Breakdown Interval, Transportation Time and Weights of Jobs   by Pankaj Kumar, Harendra Kumar, Manisha Sharma Abstract: A flow shop problem exists when all the jobs have the same processing order through the machines. In flow shop problem, the technological demand that the jobs pass between the machines in the same order. The objective of this paper is to find an optimal ordering of n jobs for 3 machines involving processing times, transportation times, break down interval and weights of the jobs by using genetic algorithm (GA) approach. The proposed algorithm is compared with already published problems in literature. The numerical results show that the present algorithm is good one within the best well known heuristic algorithms in the field. Keywords: Flow shop scheduling; processing time; break down interval; genetic algorithm. An Integrated Vendor-Buyer Model with Equal Shipments, Normally Distributed demand and Empirically Distributed lead-time   by Mehdi Seifbarghy, Salman Barzegar Abstract: This study addresses Joint Economic Lot-Sizing (JELS) Problem with a vendor and a buyer which replenishes a given product from the vendor. Shipments transferred from the vendor to the buyer are assumed to have equal sizes. In addition, unlike the previous studies, demand and delivery are assumed to be stochastic and following normal and experimental distributions respectively. In the given model, we assume that the combination of the two types of backordered and lost demand can exist. In addition, service level constraint is also considered. The purpose is to find the optimal order values, the number of shipments, reorder point and safety stock. We present a heuristic method in order to reach the decision variables of the model. Numerical examples indicated frugality in chain cost in the integrated model and efficiency in the heuristic method. In addition, the results showed that chain costs increase by increasing delivery time, but the values of reorder point and safety stock depend on the changes in the level of services and they increase as the service level increases. Keywords: Supply chain; Vendor-buyer integration; Equal Shipments; Stochastic demand; Stochastic lead-time. Modelling efficient and anti-efficient frontiers in DEA without explicit inputs   by Ali Emrouznejad, Guo-liang Yang Abstract: Data envelopment analysis (DEA) is one of the most widely used tools in efficiency analysis of many business and non-profit organisations. Recently, more and more researchers investigated DEA models without explicit input (DEA-WEI). DEA-WEI models can divide DMUs into two categories: efficient DMUs and inefficient DMUs. Usually there is a set of DMUs which are efficient so that conventional DEA models could not rank them. In this paper, we first develop a performance index based on efficient and anti-efficient frontiers in DEA-WEI models. Further, the corresponding performance index in DEA-WEI models with quadratic utility terms (quadratic DEA-WEI) is proposed also. Finally, we present two case studies on performance assessment of basketball players and the evaluation of research institutes in Chinese Academy of Sciences (CAS) to show the applicability and usefulness of the performance indices developed in this paper. Keywords: Data envelopment analysis; DEA without explicit input; efficient frontier; anti-efficient frontier. Credit financing in economic ordering policies for deteriorating items with stochastic demand and promotional efforts in two-warehouse environment   by Chandra K. Jaggi, Mamta Gupta, Sunil Tiwari Abstract: Retailers are nowadays focusing on promotional activities in order to attract customers because of increasing competition. Promotional efforts have significant impact on the replenishment policy and the sale price of goods. In this paper, a two warehouse inventory model is developed for deteriorating items subject to promotional efforts under permissible delay in payments. Here, a price dependent stochastic demand function is considered with partially backlogged shortages. Several realistic cases, sub cases and scenarios have been taken into account and the corresponding problems have been formulated as non-linear constrained optimization problems. To illustrate the proposed model two numerical examples have been solved. Further a numerical example has been extended to perform sensitivity analysis of the model and discuss specific managerial insights. Keywords: Inventory; Deterioration; Partially Backlogging; Permissible Delay in Payment; Price Dependent Stochastic Demand; Promotional Effort. Using Valid Inequalities to solve the Integrated Production-Inventory-Distribution-Routing Problem   by Noha Mostafa, Amr Eltawil Abstract: The Production-Inventory-Distribution-Routing Problem is an integrated supply chain management problem that combines decisions on several functions. The objective is to minimize the total costs without violating demand fulfilment policy. A Production-Inventory-Distribution-Routing Problem of medium size is a combinatorial optimization problem mostly intractable to solve using exact methods. The main contribution of this work is to introduce valid inequalities for a problem with a single plant, multiple products and multiple heterogeneous vehicles to improve the quality of lower bounds, obtain a good approximation of the convex hull of the polyhedron of the problem and reduce its hypervolume, so that the computation time can be reduced without a significant effect on the quality of the solutions found. Results showed that adding the valid inequalities to the model can improve the percentage gaps for all the tested instances with a significant improvement in the lower bounds from the poor bounds obtained from the Linear Programming relaxation (up to 98.8% for the data set of 50 customers and up to 79.7% for the data set of 100 customers). Keywords: PIDRP; vehicle routing; lower bounds; valid inequalities; supply chain management; Lot sizing; Inventory management; Distribution. A Non-Assignment Problem based formulation for the Asymmetric Travelling Salesman Problem and its variation   by Nahid Jafari Abstract: In this paper, an exact formulation for the Asymmetric Travelling Salesman Problem (ATSP) is presented by approaching it as a single commodity flow problem. This approach is different from existing exact formulations in the literature which are based on the Assignment Problem (AP), thus, it resolves issues that the AP-based formulations pose for solving certain real world instances by standard integer programming methods such as branch and bound. Moreover, in our computational experiments, half of the total computational time is expended to find the first feasible solution, then it is converged quickly to optimality. In contrast, the AP-based models rapidly computed an initial feasible solution but showed slow convergence to the optimum. Moreover it is extendable to other variations of the Travelling Salesman Problem (TSP) such as the multiple TSP and the selective TSP. Keywords: Asymmetric Traveling Salesman Problem; Multiple Traveling Salesman Problem; Selective Traveling Salesman Problem. Transient Solution of Fluid Queue Modulated by Two Independent Birth-death Processes   by Shruti Kapoor, Dharmaraja Selvamuthu, Arunachalam Viswanathan Abstract: The objective of this paper is to study the transient distribution of the buffer content in any intermediate node of a wireless network based on IEEE 802.11 standards. The steady state solution of the discussed model has already been given in [13]. The methodology used, maps the underlying model to a fluid queue model driven by two independent finite state birth-death processes with the aim to simplify the solution which is obtained in closed form with numerical illustration. Along with the buffer occupancy distribution, other performance measures: throughput, server utilization and expected buffer content are also obtained numerically. Keywords: IEEE 802.11 wireless networks; fluid queue; buffer occupancy distribution. Portfolio rebalancing under uncertainty using meta-heuristic algorithm   by Mostafa Zandieh, Seyed Omid Mohaddesi Abstract: In this paper, we solve portfolio rebalancing problem when security returns are represented by uncertain variables considering transaction costs. The performance of the proposed model is studied using constant-proportion portfolio insurance (CPPI) as rebalancing strategy. Numerical results showed that uncertain parameters and different belief degrees will produce different efficient frontiers, and affect the performance of the proposed model. Moreover, CPPI strategy performs as an insurance mechanism and limits downside risk in bear markets while it allows potential benefit in bull markets. Finally, using a globally optimization solver and genetic algorithm (GA) for solving the model, we concluded that the problem size is an important factor in solving portfolio rebalancing problem with uncertain parameters and to gain better results, it is recommended to use a meta-heuristic algorithm rather than a global solver. Keywords: Portfolio Rebalancing; Transaction Costs; Constant-Proportion Portfolio Insurance (CPPI); Uncertainty Theory; Meta-heuristic Algorithm. A Heuristic Search Routine for Solving Two Objective Mixed Integer LP Problems for Scheduling in a Service Factory   by Faizul Huq, M. Khurrum Bhutta, Ziaul Huq Abstract: This paper presents a two objective mixed binary integer linear programming model and a search routine solution method is proposed using a Service Factory environment with multi-processor workstations and a constant daily workload, for employee scheduling, number of machines per station, and makespan minimization objectives. The search routine is simple enough to be implemented by managers using readily available spreadsheet programs. Solution of the four station Service Factory formulation yielded results for improvement in the makespan of the shop. This search routine can be used by management in streamlining and optimizing the Service Factory production environment as exemplified in the four station case, and could also be applied to multi-processor flow shops. Keywords: Service Factory; Scheduling; Lot Splitting; Binary Integer Linear Programming; Makespan. Heuristics for the Multiple Knapsack Problem with Conflicts   by Chuda Basnet Abstract: In this paper we discuss a variant of the 0-1 knapsack problem, where there are multiple knapsacks to fill with items that have profits and sizes associated with them. The objective is to maximise the profit by selecting items to fill the knapsacks within their space constraints. In the version of the problem considered in this paper, some of the items are incompatible with each other, and cannot be placed together in the same knapsack. We apply some newly developed heuristics to the problem and compare the results with another available algorithm. Computational results are presented. The contributions of this paper are an upper bound, and the heuristics developed and tested in this paper. Keywords: Multiple knapsack; Graph colouring; Heuristic algorithms; Incompatibility constraints. The Adopting of Markov Analysis to forecast the probability of students' enrollment at universities scientific faculties in Jordan   by Yazan Migdadi, Hala Sulaiman Mahmoud Al-Momani Abstract: The aim of this research is to predict the probabilities of enrollment students of scientific faculties at public universities in Jordan, to forecast the changes of probabilities at universities' scientific faculties, to compare the differences among universities forecasted probabilities and to examine the impact of universities' location on enrolled students of scientific faculties at those universities. Secondary data were collected from the annual statistical reports of the Ministry of Higher Education and Scientific Research. 8 out of 10 public universities were surveyed, 10 scientific faculties were investigated. Markov analysis technique was used to analyze the departments efficiency. Linear regression was used to forecast the change of probabilities at universities' scientific faculties. Non-parametric statistical technique was used to compare the difference among universities forecasted probabilities and to analyze the relationship between universities' location and enrolled students. This study revealed that expected number of enrolled students of scientific faculties over time at the University of Jordan, Mu'tah University, AL al-Bayt University and AL-Hussein Bin Talal University will decrease. However, the expected number of enrolled students at the rest of universities will increase. The changes of probabilities at universities' scientific faculties were found. Significant differences of forecasted probabilities were found among some universities. It also was found that the location of university is not a determinant for expected enrolled students at all universities scientific faculties. The previous studies have not focused on investigated expected probabilities of enrolled students at universities in Jordan and not have investigated other aspects were included in this research. Keywords: Markov Analysis; forecasting; enrollment; scientific faculty; university; Jordan. Transient analysis of an M/M/c queuing system with retention of reneging customers   by Rakesh Kumar Abstract: In this paper we study the transient behavior of an M/M/c queuing system with reneging and retention of reneging customers. The probability generating function technique along with Bessel function properties is used to derive the time-dependent state probabilities explicitly. The transient behavior of system size probabilities, the expected system size, the average reneging rate, and the average retention rate is studied with the help of a numerical example. Keywords: transient analysis; M/M/c queuing system; in nite capacity; operational research; reneging; retention. Multi Criteria Decision Making Approach to Material Selection in Tribological Application   by Santosh Vitthal Bhaskar, Hari Narayan Kudal Abstract: This paper presents application of various Multi Criteria Decision Making (MCDM) techniques to material selection in tribological application. The alternative materials considered for ranking are variants of AISI 4140 which is nitrided and then coated with various low-friction surface coating materials. This study analyses and discusses the priority settings on the basis of constructed model which compares the ranking outcomes among Simple Additive Weighting (SAW), Multiplicative Analytic Hierarchy Process (MAHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), modified-TOPSIS (M-TOPSIS), and Compromise Ranking Method (VIKOR). Attribute weights obtained by Analytic Hierarchy Process (AHP), are used as the inputs and outputs are materials rankings on the basis of Material Selection Index (MSI), which help designers and engineers to reach a consensus on materials selection for a specific application. The ranks obtained by various methods are compared. Results indicate that suggested method can effectively be applied to similar problems. Keywords: Analytic Hierarchy Process;AHP; Multiplicative Analytic Hierarchy Process ; MAHP; Multi Criteria Decision Making; MCDM; Simple Additive Weighting; SAW; Technique for Order Preference by Similarity to Ideal Solution; TOPSIS; Compromise Ranking Method; VIKOR. Side constrained optimization to capture capacity of choices in the multinomial logit model: case study of income tax policy in the United States prior to the 2009 economic crisis   by Saeed Asadi Bagloee, Glenn Withers Abstract: Discrete choice models in general and multinomial logit models in particular are leading approaches in the econometric behavioral analysis. In real application, one sometimes needs to take the capacities of the choices into account. To this end we propose a convex optimization formulation in which the exponential formulation of the logit model is upheld in the Karush-Kuhn-Tucker (KKT) conditions. The capacities of the choices are then added to the formulation as side constraints. A solution algorithm based on the Successive Coordinate Descent (SCD) is proposed. For numerical evaluation, we investigate U.S. income tax policies for the years prior to the 2009 crisis. The question of interest is: how far will states go to increase the income tax share? To answer, the organic relation between the tax records of the states and employment data are captured using the logit model. Two tax sources are defined: income tax and all other tax. In the reverse-engineering approach, the total aggregated tax revenues at federal level from these two sources are made available to the states; the states are set to compete to fill their tax portfolios from the aggregated sources depending on their preferences. These two sources are limited in monetary size for which reason we employ a capacitated logit model. The numerical analysis shows that the model is able to closely replicate the income tax data. The tendency of the states for choice of income tax versus other tax sources is also assessed and it is found that: (i) all states show a propensity to levy more income tax; (ii) this propensity has a ceiling cap similar to what is already known from the Laffer Curve; and (iii) residents in the states with already high income tax are more likely to be subjected to even heavier income tax within caps. Keywords: convex optimization; Successive Coordinate Descent (SCD); logit; income tax; behavioural model; Laffer curve; economic crisis; recession. Proposing a New Approach to the Selection of Material Portfolio Using a Combination of Data Mining and Optimization Methods   by Farshad Faezy Razi, Hamed Sarkari Abstract: The present paper aims to provide a new framework for the selection of a portfolio of materials. This paper shows that, compared with the traditional methods in the selection of materials, how the new materials are analyzed based on new ideas. The case study is materials required for production of tile glaze in both traditional and modern methods. Modeling in this study was done based on mathematical description approach. The results of execution of feature selection algorithm revealed that important factors in the selection of glaze for production of tile in both traditional and modern methods include cracking, self-cleaning, uniformity, water absorption, and market potential. In addition, the results of K-means algorithm showed that all the materials of choice for production of tile glaze are not placed in a single cluster. Therefore, each cluster should be evaluated separately. Unlike the classical approaches to the selection of materials, in the new approach, candidates for the selection of tile glaze are firstly clustered by K-means algorithm. Each cluster is independently ranked using free disposal hull model. Free disposal hull is a mathematical programming model based on data envelopment analysis. The final optimized portfolio of materials was determined using the genetic algorithm. Keywords: Material selection; Feature selection algorithm; K-means algorithm; DEA-FDH; Genetic algorithm. Revenue sharing contract under asymmetric information   by Sri Vanamalla Venkataraman, Dereje Asfaw Abstract: We analyze a two stage supply chain with a single risk neutral manufacturer and a risk neutral retailer in a single period setting. The retailer associates costs towards procurement of the product and its marketing and sales. These costs are often private information of the retailer, the retailer has an incentive to overstate his associated costs to acquire a larger share of revenue. In this paper assuming retailers have private information about their associated costs we derive an optimal revenue sharing contract as designed by the manufacturer for each of the cost structure of the retailers. The retailer's choice from such a contract menu reveals information about their true cost. We analyze our model under various scenarios; we observe that the proposed revenue sharing contract improves the profit of manufacturer and that of the total the supply chain. Keywords: supply chain management; asymmetric information; revenue sharing; contract. A Decision-Making Approach for Enterprise Architecture Evolution using Simulation   by Sérgio Guerreiro, Khaled Gaaloul Abstract: Enterprise architecture (EA) is a discipline that provides management with appropriate indicators and controls to steer and model the enterprise during change. However, the management of such change is a challenging task for enterprise architects due to the complex dependencies amongst EA models when evolving from initial (As-is) to posterior (To-be) states. We present an approach supporting design decision during EA evolution, by assisting enterprise architects in computing best alternatives to a posterior state. In doing so, we model EA artifacts dependencies and identify their evolution during change. This model is, then, processed using a control schema to inform EA design decisions. Further, we rationalize on design decision by computing EA models alternatives, using Markov theory. Finally, we evaluate this decision-making approach using a motivating example by simulating a stochastic solution in order to argue about the usefulness and applicability of our proposal. Keywords: Enterprise Architecture; Evolution; Design Decision; Simulation; Markov Theories. A multiple-criteria decision analysis for criticality of boiler tube failures in interval type-2 fuzzy environment   by Ashoke Kumar Bera, Dipak Kumar Jana Abstract: This paper presents a multi-factor decision-making approach for prioritizing criticality of failure modes as an alternative to traditional approach of failure mode effect and criticality analysis (FMECA). A review of the literature reveals that although a number of studies have been done on these issues, but none of them have explicitly studied the variations in experts opinion (intra-personal uncertainty) and the variations in the understanding among experts (inter-personal uncertainty) together. To deal with this problem, this literature proposes a new fuzzy FMECA approach based on IT2 fuzzy sets, which has the ability to capture both intra-personal and inter- personal uncertainty. This approach introduces a more accurate representation of the aggregated data by presenting variations among the individual judgments into type 2 fuzzy numbers, allowing suitable weights for each risk factor by decision makers and thereby developing a flexibility for analysis. The proposed method is applied to evaluate the criticality of different failure modes of boiler tubes of a coal- Keywords: Failure mode e®ect and criticality analysis; Interval type-2 fuzzy set; Multiple-criteria decision analysis; Signed distance; Linear assignment method; Interval type-2 fuzzy number. Multi-Objective Fuzzy Quadratic Probabilistic Programming Problem Involving Fuzzy Cauchy Random Variable.   by Narmada Ranarahu Abstract: In this paper we have proposed a method for solving a multi-objective quadratic probabilistic programming problem, where the objective functions are multi-objective and quadratic in nature. The right hand side parameters are fuzzy cauchy dis- tributed independent random variable with location parameter delta and scale param- eter beta. The proposed mathematical programming problem is solved using two steps. First the fuzziness is removed by using alpha cut technique and random- ness is removed by chance constrained method. In second step, weighting method is used to solve the transformed multi-objective quadratic mathematical program- ming. This mathematical model is solved by existing methodology or software. A numerical example is presented to illustrate the efficiency and feasibility of the proposed method. Keywords: Stochastic programming; Multi-objective programming; Fuzzy programming; Cauchy random variables; Optimization techniques. A New Hybrid Supplier Selection Model   by Tuan Son Nguyen, Sherif Mohamed, Anisur Rahman Abstract: Selecting the right supplier is one of the most challenging tasks for organisations as it essentially reduces purchasing cost and improves corporate competitiveness. This study aims at developing a hybrid model in supplier selection for a non-homogeneous group decision-making process to select a supplier that best satisfies the purchaser. The analytical hierarchy process (AHP), house of quality (HOQ), and linguistic ordered weighted averaging (LOWA) operator are applied in the proposed model. This model is illustrated with a real world example by applying it to a mechanical manufacturing company in Vietnam. It is found that supplier selection does not only depend on a low price offer, but also on suppliers quality, technological capability, capability of on-time delivery, flexibility and good relationship. This study makes new methodological and practical contributions to supplier selection research and applications through development of a hybrid model for non-homogeneous group decision-making in supplier selection, and for the first time this study applies the LOWA operator in aggregating linguistic terms of non-homogeneous group decision-making in a supplier selection process. Keywords: supply chain management; decision making; supplier selection; analytic hierarchy process; house of quality; linguistic ordered weighted averaging. On the application of Bayesian Credibility Theory in Movie Rankings   by Palash Ranjan Das, Gopal Govindaswamy Abstract: Credibility theory is a branch of actuarial science devoted to quantify how unique a particular outcome will be compared to an outcome deemed as typical. In this paper, we will examine the application of the principles of Bayesian Credibility Theory in rating and ranking movies by a premier online movie database based on users votes. Although the Bayesian credibility theory was developed originally as a method to calculate the risk premium by combining the individual risk experience with the class risk experience, it is generic enough to deal with a wide range of practical applications quite different from the classical application mentioned above. One such diverse application of the theory in an unlikely domain will be discussed in this paper. Keywords: Credibility Theory; Prior distribution; Likelihood function; Posterior distribution; Loss function; Bayesian approach. Determinants of Indian banks efficiency: A Two-stage approach   by Jayaraman A.R, Srinivasan M.R Abstract: Analyzing the performance of banks at periodical intervals assumes importance from the perspective of bankers, investors and regulator. This study seeks to examine the cost, revenue and profit efficiency of Indian banks during 2004-2013 using Data Envelopment Analysis (DEA) and identifies the determinants of efficiency using Tobit regression. Results show that the cost and profit efficiency of banks are positively correlated and reveal that if the banks are cost efficient, they are also profit efficient. Further, profit efficiency is the better differentiator of performing and non-performing banks, in Indian context. The main determinants of efficiency of banks under cost, revenue and profit DEA models are size and management of the banks. Contrary to popular belief, the GDP growth has an inverse relationship with efficiency of the banks. Keywords: Bank; DEA; Cost Efficiency; Profit Efficiency; Tobit Regression.DOI: 10.1504/IJOR.2019.10010959  Analysis of an M/M/c Queue with Heterogeneous Servers, Balking and Reneging   by R. Sudhesh, A. Azhagappan Abstract: This paper analyzes a heterogeneous multi-server queuing system with balking and reneging. In this system, when all the c servers are busy, an arriving customer decides either to join the queue with probability p or balk with probability 1 p. The customers waiting in the queue become impatient due to the long wait for service. Therefore, each individual waiting customer activates an independent impatience timer such that the customers service starts before his timer expires, he gets the service and leaves the system after the completion of service. Otherwise, he abandons the system and never returns. The time-dependent system size probabilities are derived explicitly using generating function. Further the time-dependent mean, variance, busy period distribution and steady-state probabilities are obtained. Finally, some numerical illustrations are presented. Keywords: M/M/c queue; heterogeneous servers; balking; reneging; transient probabilities; Busy period; Generating functions. Solution of a sustainable bi-objective book-producers problem using statistical approach   by Adrijit Goswami, Snigdha Karmakar, Sujit Kumar De Abstract: This article leads to statistical approach on bi-objective economic production quantity (EPQ) inventory problem especially on two book producers problem under unit selling price and production run time dependent demand rate. The concept of early product-early demand and low price-high demand policy has been employed for developing this bi-objective inventory problem. The discounts on marked unit selling price have been offered at the time of selling the books on spot for both the producers also. In this model we are optimizing the objective functions of both producers for this it is considered as bi-objective model. However, behind any computational process there might have the effects of extraneous variables for which we have used correlation approach to solve the model. In addition, we use Goal Attainment (GA) approach to solve the bi-objective problems first and then generate data set from sensitivity analysis of the model. Moreover, we compute the correlation coefficients matrix for both joint and independent relations of the objective functions. The decision is made on the basis of testing of hypothesis over the decision maker (DM)s zone of intelligence. Finally, the dot plots are made for justification of the model. Keywords: Bi-objective inventory; Discounts; Goal Attainment Method; Extraneous variables; Spearman’s correlation coefficient; Optimization. Length Of Stay Reduction in the Emergency Department and its quantification Using Complex Network Theory   by Antonio Del Torto, Rossella Pozzi, Emanuele Porazzi, Elisabetta Garagiola, Fernanda Strozzi Abstract: Overcrowding in Emergency Department (ED) has become an increasingly significant problem worldwide. Different crowding measures have been proposed in the literature and, between them, the Length of Stay (LOS) is one of the most recognised. In this paper the LOS in the ED of a Hospital located in the Southern central region of Italy is calculated; than the ED process is represented as a network and, due to the identification of the bottleneck node, possibilities to reduce the LOS through changes to the actual process can be identified. The present work demonstrates that the LOS reductions obtained by process changes and measured by the analytical LOS calculations can be assessed also through the changes in the topological properties of the activities network, that are measured using complex network measures. Keywords: network; emergency department; Length of Stay; overcrowding.DOI: 10.1504/IJOR.2019.10011175  Reliability appraisal for consecutive-k-out-of-n:F system of non-identical components with intuitionistic fuzzy set   by Akshay Kumar, S.B. Singh, Mangey Ram Abstract: In this paper, the reliability of a linear (circular) consecutive k-out-of-n:F system of non-identical elements have been obtained with the help of intuitionistic fuzzy concept and Weibull lifetime distribution. The calculation of parameters of the Weibull distribution is presented by intuitionistic triangular fuzzy numbers. The Markov chain technique is employed to compute the reliability of the transition state of the system. A numerical example is also illustrated for demonstrating the reliability of the system. Keywords: Linear (Circular) (k; n:F) system; reliability; Weibull distribution; intuitionistic fuzzy numbers. University course timetabling problem considering day and time pattern   by Chompoonoot Kasemset, Takashi Irohara Abstract: The university course timetabling problem (UCTTP) is a timetabling problem faced by many educational institutes. The characteristics of the UCTTP of one university are different from those of another as these are dependent on the regulations of the particular institute. This study aimed to propose a new formulation for the UCTTP when a day and time pattern is introduced. The day and time pattern would be set by the university and all assigned courses should follow this pattern. The different points of the proposed formulation would structure the model using starting and ending timeslots instead of a single timeslot to deal with courses with consecutiveness, periodic repeat, and multi-period sessions. The formulation was developed, and then verified and validated using a small-size problem. To present the effectiveness of the proposed model, three test cases were solved and the results were compared with the general formulation of the UCTTP in terms of solution quality, the number of variables and constraints, and computational time using a commercial solver. The test results showed that the same optimal solution can be derived and the number of variables and constraints reduced with less computational time compared to the general formulation. Then, the case study was solved using the proposed formulation with multiple objectives based on goal programming (GP). The solution of the case study was compared with the current timetable. The results of the case study show that the number of assignments during the undesirable time was minimised and that the total satisfaction score of all the lecturers was improved by 7.95% by the proposed model. Keywords: university course timetabling; integer programming; goal programming; case study; day and time pattern. Robust Optimization of Unconstrained Binary Quadratic Problems   by Mark Lewis, Gary Kochenberger, John Metcalfe Abstract: In this paper we focus on the unconstrained binary quadratic optimization model, maximize x^t Qx, x binary, and consider the problem of identifying optimal solutions that are robust with respect to perturbations in the Q matrix.. We are motivated to find robust, or stable, solutions because of the uncertainty inherent in the big data origins of Q and limitations in computer numerical precision, particularly in a new class of quantum annealing computers. Experimental design techniques are used to generate a diverse subset of possible scenarios, from which robust solutions are identified. An illustrative example with practical application to business decision making is examined. The approach presented also generates a surface response equation which is used to estimate upper bounds in constant time for Q instantiations within the scenario extremes. In addition, a theoretical framework for the robustness of individual x_i variables is considered by examining the range of Q values over which the x_i are predetermined. Keywords: Robust Optimization; Unconstrained Binary Quadratic Problems; Upper Bounds; Business Decision Making; Scenario Generation; Experimental Design; Surface Response Equation; Sensitivity Analysis. Increased Flexibility in Multi Echelon Multi Capacitated Supply Chain Network Design   by Sahand Ashtab, Richard Caron, Esaignani Selvarajah Abstract: The multi echelon, multi capacitated supply chain network design challenge is to determine the numbers, locations and capacity levels of plants and warehouses; as well as the product flow from plants to warehouses and then from warehouses to customer zones in order to meet demand at minimum cost. Mathematical models for multi capacitated supply chain network design provide a finite set of capacity levels from which to choose; and include variables and constraints to ensure the selection of a single capacity level for each facility to be built. By eliminating the constraints that enforce a single capacity selection, we allow for the selection of several capacity levels for a single plant or warehouse. If such a selection occurs, the plant or warehouse is built with size equal to the sum of the selected capacity levels. This gives an exponential increase in the number of available capacity levels. The increased flexibility allows for less costly supply chain network designs. We present numerical results that demonstrate improved solutions, that is, lower cost solutions, with lower computational effort. Keywords: Facility planning and design; Supply chain network design; Facility location; Mixed integer linear program; Multi echelon; Multi capacitated. Environmentally-adjusted efficiencies of Vietnamese Higher Education Institutions: A Multi-stage Bootstrap DEA Method   by Carolyn Tran, Renato Andrin Villano Abstract: This paper analyses the operational efficiencies of Vietnamese higher education institutions (HEIs) after three decades of transition to the market-orientated economy. Using data from 112 universities and 141 colleges in the period 20112013, a new stage is proposed to integrate the bootstrap procedure into the environmentally-adjusted multi-stage DEA approach to measure the efficiencies of HEIs. The findings indicate that the efficiencies of HEIs are relatively low and are strongly affected by environmental variables, namely, ownership, location, age and financial capacity. Some managerial implications are discussed in improving the performance of HEIs. Keywords: Efficiency; data envelopment analysis; bootstrap; universities; colleges; Vietnam. A New Method for Solving Linear Fractional Programming Problem with Absolute Value Functions   by Sapan Das, Tarni Mandal, S.A. Edalatpanah Abstract: In this paper, we propose a new model for linear fractional programming problem with absolute value functions. The major contribution of this paper is that transformation of linear fractional programming problem into separate linear programming problems with some theorems and then solution of these problems by popular algorithm. This work also used to simplex type algorithms to arrive at an optimal solution for a linear programming problem with absolute value. Moreover, we compare this method with an existing method. Numerical experiments are also given to illustrate the assertions. Keywords: Fractional programming; computing science; absolute value; simplex method. A Comprehensive Merit Aid Allocation Model   by Paul K. Sugrue Abstract: This paper highlights the development of a merit-based financial aid allocation model for a large private university incorporating both yield rate prediction and optimal fund distributions. . The objective used in the optimal allocation is the average SAT score of the incoming class. In the application, the allocation decision is bound only by the financial aid budget and the number of accepted applicants in homogeneous SAT score groupings. Required yield rates are estimated utilizing logistic regression with SAT score and merit aid award levels as the exogenous variables. The parameter estimates are based upon data from the previous year. Comparing the actual result with the model result shows a 17.3 point increase in the mean SAT score, which is shown as equivalent to a 20% increase in the merit aid budget. Keywords: Financial aid; Yield rates; Merit based aid; Binary logistic regression; Linear programming. MCGL: A New Reference Dependent MCDM Method   by Ram Kumar Dhurkari Abstract: This paper proposes a method for discrete alternative multi-criteria decision-making (MCDM) under certainty. The proposed method (Multi Criteria Gain Loss: MCGL) is based upon the tenets of prospect theory and norm theory. The two major objectives for the development of the MCGL method are 1) to reduce the complexity of the MCDM in order to improve the performance of the decision maker (DM) in the process of judgment, and 2) to use some of the latest descriptive theories of decision making in order to improve the effectiveness of the MCDM method in terms of its resemblance with actual decisions. Two studies conducted to test the effectiveness of the proposed method in resembling actual or real decisions. In comparison to the Analytic Hierarchy Process (AHP), the MCGL method is able to capture individuals decision-making process more accurately. Applicability measures like the number of decisions required, time and the cognitive burden strongly favours the MCGL method. Keywords: Multi-Criteria Decision Making; Prospect Theory; Analytic Hierarchy Process. Path anticipation and prioritized conflict-free train re-scheduling on a linear network   by Nitin Sakhala, Ajinkya Tanksale, Jitendra Jha Abstract: Rail schedule may get disturbed due to unforeseen set of events, which requires a quick response to plan a new feasible schedule under the given set of complicating constraints, and resolving the potential conflicts among trains. This gives rise to the classical train timetable re-scheduling (TTR) problem, which is combinatorial in nature, computationally challenging. In this work, we present the macroscopic train orientation of TTR problem with explicit consideration of safety characteristics. In case of disturbances, path anticipation criteria are used to generate a feasible and conflict-free schedule. A novel algorithm based on inhibitor net to prioritizing trains and conflict-resolution is presented. We demonstrate the application of a decision support system with the controllers intervention for the considered problem. Finally, the proposed solution approach is tested for its efficiency on several test instances generated for a real-life case of a single line corridor in the Indian rail network. Keywords: Train re-scheduling; disturbance handling; conflict-resolution; Petri net. Efficient Algorithms to Match GPS Data on a Map   by Renaud Chicoisne, Fernando Ordoñez, Daniel Espinoza Abstract: Estimating the distribution of travel times on a transportation network from vehicle GPS data requires finding the closest path on the network to a trajectory of GPS points. In thiswork we develop 1)An efficient algorithm (MOE) to find such a path and able to detect the presence of cycles, and 2)A faster but less accurate heuristic (MMH) unable to detect cycles.We present computational results that compare these algorithms, for different sampling rates and GPS sensitivities, using GPS trajectories of three networks: a grid graph and street networks of Santiago and Seattle.We show that MOE (MMH) returns in seconds (hundredths of second) paths where on average 93% (91%) of the edges are within a corridor of one meter from the real path. Keywords: Map Matching; Travel Time Estimation. An efficiency analysis of food distribution system through Data Envelopment Analysis   by Claudia Paciarotti, Maurizio Bevilacqua, Filippo Emanuele Ciarapica, Giovanni Mazzuto, Leonardo Postacchini Abstract: The specific quality and safety requirements, typical of the food supply chain, force to strong action on implementing distribution networks, reducing transport and delivery costs, improving distribution efficiency and performance, increasing carriers control and flexibility. In this context, the selection and evaluation of third-party logistics has become a crucial aspect in order to realise an efficient food products distribution, with both a high level of service and competitive costs. This paper implements Data Envelopment Analysis theory to analyse the case of an Italian food producer, which distributes its products on the national territory, through several third-party logistic carriers. This study made possible to define the most efficient carrier among those responsible for the distribution process, analysing the retail trade and large-scale retail trade. This paper represents a reference guideline to all the food companies involved in the process of evaluation and improvement of the distribution process. Keywords: Data Envelopment Analysis; efficiency measurement; food supply chain; supply chain performance; third-party logistics; food deliveries evaluation; transport logistics; logistics performance; decision making units; carriers ranking; carriers selection; transport of perishable products. Closed parasitic flow loops and dominated loops in networks   by Michael Todinov Abstract: This paper raises awareness of the presence of closed parasitic flow loops in the solutions of almost every published algorithm for maximising the throughput flow in networks. These are highly undesirable loops of flow which effectively never leave the network. The paper also demonstrates the presence of dominated parasitic flow loops in the solutions based the time-honoured successive shortest path approach. It is shown that even in a network with multiple origins and a single destination, the successive shortest path strategy fails to minimise the total length of the routes if the capacity of the delivery channels is limited. The paper demonstrates that the probability of existence of closed and dominated parasitic flow loops in networks is surprisingly high. Accordingly, an algorithm for eliminating all dominated flow loops in large and complex networks is proposed. Keywords: parasitic flow loops; maximum throughput flow; successive shortest paths; multiple interchangeable origins; multiple destinations. Scheduling batches with time constraints in wafer fabrication   by Giovanni Pirovano, Federica Ciccullo, Margherita Pero, Tommaso Rossi Abstract: This work proposes and tests an algorithm for batching and dispatching lots along cleaning and diffusion operations of a wafer fab. These are characterized by (i) time constraints (i.e. the time between the end of an operation n and the start of the operation n+q must be lower than a time-limit, in order to guarantee the lots quality) and (ii) absence of batching affinity between operations. Literature so far has been falling short in proposing scheduling algorithms suitable for this context. Therefore, we propose two heuristic algorithms to minimize the average flow time and the number of re-cleaned lots, maximize machine saturation, and avoid scrapped lots. Discrete-event simulation was used to test the performance of the two algorithms using real data of STMicroelectronics. The formerly proposed model outperforms the latter. Therefore, STMicroelectronics implemented the former in its fab in Catania gaining an increase in the average Overall Equipment Effectiveness of 7%. Keywords: semiconductor manufacturing; dispatching rules; batch; scheduling; wafer fab; time constraints; diffusion; STMicroelectronics. A Note on Min-Max Goal Programming Approach for Solving MultiObjective De Novo Programming Problems   by Susanta Banik, Debasish Bhattacharya Abstract: Min-max goal programming approach for solving multiobjective De Novo Programming Problems was studied by Nurullah Umarusman in 2013.The present study is a further attempt to examine the approach and present an improved version of the approach. In Nurullahs method, each of the goal constraints are having both positive and negative deviation variables, whereas in the proposed approach only one deviation variable has been used. The method of solution has been illustrated with the numerical examples. The solution obtained by proposed method yields objective values which are better than those obtained by Nurullah for the same set of weights. Keywords: Optimal system design; De Novo programming; Min-max goal programming; Multi-objective Optimization. A continuous approximation procedure for determining inventory distribution schemas within supply chains: Gradual and intermittent shipping patterns   by Faizul Huq, Trevor Hale, Nikhil Pujari Abstract: The popularity of supply chain integration models are increasing. The research in this paper builds upon prior research and presents an integrated inventory supply chain optimization model that incorporates the issues of location, production, inventory, and transportation simultaneously. The objective of the current research is to determine the optimal number as well as the optimal size of shipments under a variety of production and shipping rate scenarios. Previous research in this area assumed instantaneous shipping. Herein, this assumption is generalized to include a non-instantaneous, gradual shipping pattern as well as staggered, more intermittent shipping pattern. These two more generalized shipping scenarios (each with several sub-scenarios) are investigated and closed form expressions for the optimal number and the optimal size of shipments for each scenario are obtained. A detailed numerical example is presented to demonstrate the efficacy of the approach. Keywords: Distribution system; inventory management; supply chain; continuous approximation; shipping pattern. New Approach for Ranking Efficient DMUs based on Euclidean Norm in Data Envelopment Analysis   by M.E. Bolori, Shokorlla Ziyari, Ali Ebrahimnejad Abstract: Data Envelopment Analysis (DEA) is a widely used technique for measuring the relative efficiencies of Decision Making Units (DMUs) with multiple inputs and multiple outputs. In many applications, ranking of DMUs is an important and essential procedure to decision makers in DEA, especially when there are extremely efficient DMUs. Basic DEA models usually give the same efficiency score for some DMUs. Hence, it is necessary to rank of all extreme DMUs. The motivation of this work is to propose an appropriate method in order to overcome the drawbacks in several methods for ranking DMUs based on the DEA concept. In the present paper, we propose a model for ranking extreme efficient DMUs in DEA by super efficiency technique and Euclidean norm (l2-norm). The presented method in this paper is able to overcome the existing obstacles in some methods. As regards, the proposed model is into nonlinear programming form, a linear model is suggested to approximate the nonlinear model. Keywords: Data Envelopment Analysis (DEA); Ranking; Efficiency; Extreme Efficient; Euclidean norm. A Perturbation-based Approach for Continuous Network Design Problem with Link Capacity Expansion   by Robert Msigwa, Lu Yue, Li-Wei Zhang Abstract: This paper formulates a continuous network design problem (CNDP) as a nonlinear mathematical program with complementarity constraints (NLMPCC), and then a perturbation-based approach is proposed to overcome the NLMPCC problem and the lack of constraint quali fications. This formulation permits a more general route cost structure and every stationary point of it corresponds to an global optimal solution of the perturbed problem. The contribution of this paper from the mathematical perspective is that, instead of using the conventional nonlinear programming methodology, the variational analysis is taken as a tool to analyze the convergence of the perturbation-based method. From the practical point of view, a convergent algorithm is proposed for the CNDP and employ the sequential quadratic program (SQP) solver to obtain the solution of the perturbed problem. Numerical experiments are carried out in both 16 and 76-link road networks to illustrate the capability of the perturbation-based approach to the CNDP with elastic demand. Results showed that the proposed model would solve a wider class of transportation equilibrium problems than the existing ones. Keywords: Continuous network design problem; Bilevel programming; Nonlinear mathematical program with complementarity constraints; Variational analysis; Perturbation-based approach. On Multi-level Quadratic Fractional Programming Problem with Modified Fuzzy Goal Programming Approach   by Kailash Lachhwani Abstract: This paper addresses new modified algorithm for solving multi-level quadratic fractional programming problem (ML-QFPP) based on fuzzy goal programming (FGP) approach with some major modifications in the traditional fuzzy goal programming technique suggested for multi-level multi objective linear programming problems (ML-MOLPPs). In this modified approach, suitable linear and non linear membership functions for the fuzzily described numerator and denominator of the quadratic objective functions of all levels as well as the control vectors of higher levels are respectively defined using individual optimal solutions. Then fuzzy goal programming approach is used for the achievement of highest degree of each of the membership goal by minimizing the negative deviational variables. The proposed modified algorithm simplifies the ML-QFPP by eliminating solution preferences by the decision makers at each level, thereby avoiding large computational difficulties associate with multi-level programming problems and decision deadlock situations. The aim of this paper is to present simple and efficient technique to obtain compromise optimal solution of ML-QFP problems with all major types of membership functions. Comparative analysis over the variation in the types of membership functions is also carried out with numerical example to show suitability of different membership functions in the proposed algorithm. Keywords: Multi-level Quadratic Fractional Programming; Fuzzy Goal Programming; Membership Function; Negative deviational variable; Compromise optimal solution. Solving a class of multiobjective bilevel problems by DC programming   by Aicha Anzi, Mohammed Said Radjef Abstract: In this paper, we consider a class of multiobjective bilevel programming problems in which the first level objective function is assumed to be a vector valued DC function and the second level problem is a linear multiobjective program. The problem is transformed into a standard single optimization problem by using a preference function. We give a characterization to the induced region and reformulate the problem as a problem of optimizing a function over the efficient set. Next, a well known representation of the efficient set is used which will allow to transform the problem, using an exact penalization, into a DC program. Finally, we apply the DC Algorithm to solve the resulting DC program. Keywords: Bilevel programming; multiobjective optimization; exact penalty; DC Algorithm; DC programming; preference function. Analytical Insights into Firm Performance: A Fuzzy Clustering Approach for Data Envelopment Analysis (DEA) Classification   by Amir Karbassi Yazdi, Yong Wang, Abotorab Alirezaei Abstract: Many companies use Data Envelopment Analysis (DEA) as a method for measuring performance and benchmarking with other organizations. The aim of this study is to describe a new approach for data envelopment analysis (DEA) classification based on fuzzy clustering. The new method is used for clustering Decision Making Units (DMUs) and rank them from the least priority cluster to highest priority cluster. Thus, inefficient clusters can be identified as compared to efficient clusters. This study evaluates 25 insurance companies based on output oriented CCR methods, and the result shows that 10 companies belong to the efficient cluster. Thus, decision makers in the inefficient cluster can benchmark with their efficient counterparts to achieve better performance. Keywords: Data Envelopment Analysis (DEA); Fuzzy Clustering; Triangular Fuzzy Number; Insurance Company; Performance; Data Analysis; Decision Making Unit (DMU); Industry Analysis; Efficiency; Cluster. Gravitational Search Algorithm (GSA) based UPQC for Power Quality Improvement of WECS   by R. Anitha, S. Jeyadevi Abstract: The design of merged presentation of Unified Power Quality Compensator (UPQC) and wind energy conversion system (WECS) is conscientious for extenuating the power quality (PQ) problems of distribution scheme in the work. The projected scheme is unruffled of WECS, sequences and shunt active power filters (APF) joined to DC link that is capable to compensate the voltage sag, swell, harmonics and voltage interruption. The inoculation of wind power into an electric grid gives the PQ problems and these are perceived. Currently, the recompense approach of UPQC is examined with GSA. Here, GSA is engaged to enhance the control pulses of UPQC. The expected technique time-honoured the optimal control pulses of the sequences and shunt active power filter (APF) on the basis of the source side and load side qualities. These qualities are engaged to the inputs of the predicted algorithm and the error values are ballpark from the source side and load side parameter. To acquire optimal performance of the distribution system, these faults are diminished and producing the optimal control signals. The expected scheme is capable to bring in the active power to grid also its competence in augmentation of power quality in distribution scheme. The presentation of the expected GSA based UPQC scheme is corroborated over simulations by MATLAB/SIMULINK and compared with the traditional approaches such as adaptive neuro-fuzzy inference system (ANFIS) based UPQC and genetic algorithm (GA) based UPQC. The simulation solutions are portrayed for valuation of various control approaches and by performing FFT scrutiny Total Harmonic Distortions (THD) are calculated. Keywords: UPQC; GSA; ANFIS; GA; series & shunt APF; voltage; current; real and reactive power. Hybrid Approach for a Reliable buffer-less OBS Network with Reduced end-to-end delay and Burst loss   by Bharathi Lakshmanan, Sasikala Ramasamy, Srinivasan Alavandar Abstract: Optical Burst Switching (OBS) is a very efficient all optical transmission network. But the performance of the network may reduce because of the burst losses. Hence to eliminate the collision and dropping of packets at the core nodes we have proposed a Hybrid approach for a reliable buffer-less OBS network known as an Enhanced Multipath Adaptive Burst Assembly Algorithm (EMP-ABAA). In this technique based on the priority and type of users (i.e. regular users with lesser priority and premium users with high priority), data packets are routed efficiently. At the core nodes the relative drop in data packet, delivery ratio, delay and energy consumption is evaluated in comparison with FAHBA approach. From the simulation results, using NS2 simulation, it is observed that the proposed approach outperforms FAHBA approach; hence enhancing the efficiency and reliability of the OBS network with lesser overhead utilization in the network. Keywords: OBS; Core nodes; Latency; Fuzzy logic; Routing. Solutions of multiple objective linear programming problems by applying T-sets in imprecise environment   by Arindam Garai, Palash Mandal, Tapan K. Roy Abstract: In this paper, technique to find Pareto optimal solutions to multiple objective linear programming problems under imprecise environment is discussed. In 2015, Wu et al redefined membership functions of fuzzy sets.. But under uncertainty, we observe that prime intention of maximizing up-gradation of most misfortunate is better served by removing some constraints from mathematical models, which are obtained by applying existing fuzzy optimization technique. Further in existing fuzzy optimization technique, membership functions are not utilized as per definitions. Moreover, in existing fuzzy optimization technique, some constraints may make model infeasible. Consequently, here, new function viz. T-characteristic function is introduced to supersede membership function and subsequently new set viz. T-set is introduced to supersede fuzzy set for representing uncertainty. And one general algorithm is developed to find Pareto optimal solutions to multiple objective linear programming problems by applying newly introduced T-sets. Numerical examples further illustrate proposed algorithm. Finally conclusions are drawn. Keywords: Decision making under uncertainty; Fuzzy sets; Fuzzy mathematical programming; Multiple objective linear programming; Pareto optimal solutions; T-characteristic functions; T-sets. Power Quality Improvement by UPQC Using ANFIS Based Hysteresis Controller   by R. Manivasagam, R. Prabakaran Abstract: In this paper, an adaptive neuro fuzzy interference system (ANFIS) that is based on hysteresis controller is being proposed for achieving the power quality improvement. The innovatory ideas behind this methodology are the smoothness obtained with the fuzzy interpolation and the adaptability for complex problems using the neural network back propagation. In addition, the neural network renders increased control over the output voltage of the series active power filter (APF) and the output current of the shunt APF too. Here, the ANFIS is trained using the target control signals of both the series APF as well as the shunt APF and with the corresponding input source side and load side parameters of the system. During the testing time, the UPQC is controlled using the control signals that are attained from the ANFIS. With the utilization of the proposed method, the voltage and the current perturbations are reduced and the system power quality is enhanced. The MATLAB/Simulink platforms are used to execute the proposed control technique and the presentation is examined using different types of source voltage fault conditions. The effectiveness of the proposed ANFIS based controller is analyzed through the comparison analysis with the conventional control techniques. Keywords: UPQC; ANFIS; power quality; series APF; shunt APF; voltage; current. On the Existence of a Finite Linear Search Plan with Random Distances and Velocities for a One-Dimensional Brownian Target   by Mohamed El-Hadidy Abstract: In this paper, we consider a linear search model that takes into consideration the velocities and the distances which the searcher do them are independent random variables with known probability density functions (PDFs). The searcher moves continuously along the line in both directions of the starting point (origin of line). We use the Fourier-Laplace representation to give an analytical expression for the density of the random distance in the model. Also, we get the conditions that make the expected value of the first meeting time between the searcher and the target is finite. Keywords: Linear search problem; Finite search plan; One-dimensional Brownian motion; Fourier-Laplace transform. Performance of MIMO-OFDM Systems   by K. Vidhya Abstract: Orthogonal Frequency Division Multiplexing (OFDM) is one of the new modulation techniques which is used to combat the frequency-selectivity of the transmission channel models achieving high data rate without intersymbol interference. OFDM may be combined with antenna arrays at the transmitter and receiver to increase the system capacity on time-variant and frequency-selective channel models resulting in a multiple-input multiple-output (MIMO) configuration. In this paper, SISO, SIMO, MISO and MIMO-OFDM configurations of OFDM systems are proposed. LS channel estimator is used to calculate the channel coefficients. The four different OFDM systems are analyzed and simulated. The simulation consists of four parameters namely bit error rate, mean square error, symbol error rate and capacity of the channel for MIMO-OFDM systems. The error rate values are minimized in 2x2 MIMO-OFDM systems compared to other 1x1, 1x2, 2x1 OFDM systems. Similarly channel capacity is maximized in 2x2 MIMO-OFDM systems, compared to the other OFDM systems. These performances are implemented using MATLAB software. Keywords: multiple-input multiple-output; OFDM systems; SISO; SIMO. Fuzzy Reliability Redundancy Optimization with Signed Distance Method for Defuzzification Using Genetic Algorithm   by Sanat Kumar Mahato, Nabaranjan Bhattacharyee, Rajesh Paramanik Abstract: Consideration of impreciseness is more realistic for modeling of physical phenomena. This impreciseness can be considered in several ways like, interval/stochastic/fuzzy or mixture of these. In this work, we have taken for optimizing of the system reliability of a redundancy allocation problem formulated from a complex network system with imprecise parameters in the form of trapezoidal fuzzy numbers (TrFN). The signed distance method has been used to defuzzify the fuzzy values. Then Big-M penalty technique is used to transform the problem to unconstrained optimization problem. To solve these problems, we have implemented the real coded elitist genetic algorithm (RCEGA) for integer variables with tournament selection, intermediate crossover and one neighborhood mutation. For illustration, the five link bridge network system has been solved and the results have been presented. Keywords: Reliability-redundancy allocation; Imprecise environment; Genetic Algorithm; Fuzzy number; Defuzzification; Signed distance method; Penalty function. Design optimization of vehicle suspension systems using artificial intelligent techniques   by Vivek Kalyankar, Ajinkya Musale Abstract: Suspension system plays important role in automobiles and to some extent it is treated as backbone of vehicles. Design of suspension systems present challenges because of different conflicting criterias and hence, optimum design of its parameters is essential to get better ride comfort. Important design parameters involved in suspension systems are un-sprung mass, sprung mass, tire stiffness, spring stiffness, suspension damping coefficient, etc.; and obtaining optimum design combination of all these parameters is only possible with the use of appropriate optimization techniques. This article presents the summary of various optimization techniques used by previous researchers for design optimization of suspension systems. It is observed that, despite having various evolutionary optimization techniques, most of the earlier work was surrounded with traditional methods and genetic algorithm. Hence, a better performing algorithm compared to those, is demonstrated here to prove, uses of appropriate algorithm will help to improve the performance of suspension systems. A swarm based artificial bee colony algorithm is considered here to achieve optimum design and it is demonstrated with two examples having different road conditions. Results obtained shows considerable improvement in the design of suspension system thereby achieving a better ride comfort when compared with the results of previous researchers. Keywords: Design optimization; ABC algorithm; Vehicle model; Degree of freedom; Suspension system; Ride comfort. An Extended Multi-Objective Capacitated Transportation Problem with Mixed Constraints in Fuzzy Environment   by Srikant Gupta, Irfan Ali, Aquil Ahmed Abstract: In this paper, we study a multi objective capacitated transportation problem (MOCTP) with mixed constraints. This paper is comprised of the modeling and optimization of a MOCTP in fuzzy environment in which some goals are fractional and some are linear. In real life application of the fuzzy goal programming problem with multiple objectives, it is difficult for the decision maker(s) to determine the goal value of each objective precisely as the goal values are imprecise or uncertain. Also, we developed the concept of linearization of fractional goal for solving the MOCTP. In this paper imprecision of the parameter is handled by the concept of fuzzy set theory by considering these parameters as trapezoidal fuzzy number. α cut approach are used to get the crisp value of the parameters. Numerical examples are used to illustrate the method for solving MOCTP. Keywords: Capacitated Transportation Problem; Multi Objective Linear Programming; Multi Objective Fractional Programming; Mixed Constraints; Trapezoidal Fuzzy Number; Fuzzy Goal Programming. Multi-Objective Fixed-Charge Transportation Problem Using Rough Programming   by Sudipta Midya, Sankar Kumar Roy Abstract: This paper analyzes the Multi-Objective Fixed-Charge Transportation Problem (MOFCTP) using rough programming. Due to globalization of market, the parameters of MOFCTP may not be defined precisely, so the parameters of the MOFCTP are treated as rough intervals. Expected value operator is used to convert rough MOFCTP to deterministic MOFCTP. Fuzzy programming method and linear weighted-sum method are used to obtain Pareto-optimal solution from deterministic MOFCTP. A comparative study is made between the obtained solutions extracted from the methods; and thereafter we perform a procedure to analyze the sensitive analysis of the parameters in MOFCTP. Finally, in order to show the applicability of our proposed study, an example on MOFCTP is included in this paper. Keywords: Fixed-charge transportation problem; Rough programming; Fuzzy programming; Multi-objective programming; Pareto-optimal solution. On exact solution approaches for concave knapsack problems   by Stephan Visagie, Liezl Van Eck Abstract: This paper introduces five characteristics of concave knapsack problem (CKP) instances that influence computational times of algorithms. A dataset, based on these characteristics, is randomly generated and made available online for future studies and comparison of computational times. In this study the dataset is used to compare the computational performance of two integer programming formulations and four algorithms to solve CKPs. A novel algorithm (BLU) that combines the logic of dynamic programming and the Karush-Kuhn-Tucker necessary conditions for the CKP is also introduced. The computational times for the two integer programming formulations were too long and were thus excluded from the statistical analysis. Analysis of the computational times shows that algorithms are sensitive to different characteristics. Any algorithm, depending on the settings of the five characteristics, could win in terms of average computational time, but BLU outperforms the other algorithms over the widest range of settings for these characteristics. Keywords: Concave knapsack problem; branch-and-bound; dynamic programming; comparison of algorithms. Intuitionistic fuzzy zero point method for solving type-2 intuitionistic fuzzy transportation problem   by Senthil Kumar Abstract: In conventional transportation problem, supply, demand and costs are fixed crisp numbers. Therefore in this situation the decision- maker (DM) can predict transportation cost exactly. On the contrary, in real world transportation problems the costs are in uncertain quantities with hesitation due to various factors like variation in rates of fuels, traffic jams, weather in hilly areas etc. In such situations the DM cannot predict transportation cost exactly and it will force the DM to hesitate. So, to counter these uncertainties, in this article, the author designed a transportation problem in which supplies, demands are crisp numbers and cost is intuitionistic fuzzy number. This type of problem is termed as type-2 intuitionistic fuzzy transportation problem (type-2 IFTP). Hence to deal with uncertainty and hesitation in transportation problem, intuitionistic fuzzy zero point method is proposed to find out optimal solution to the type-2 IFTP. Moreover, special kind of type-2 IFTP is proposed and its related theorems are proved. Finally, the ideas of the proposed method are illustrated with the help of numerical example which is followed by the results and discussion. Keywords: Intuitionistic Fuzzy Set; Triangular Intuitionistic Fuzzy Number; Trapezoidal Intuitionistic Fuzzy Number; Type-2 Intuitionistic Fuzzy Transportation; Intuitionistic Fuzzy Zero Point Method; Optimal Solution. Supply chain management under product demand and lead time uncertainty   by Joaquim Jorge Vicente, Susana Relvas, Ana Paula Barbosa-Póvoa Abstract: This paper considers a multi-echelon inventory/distribution system formed by N-warehouses and M-retailers that manages a set of diverse products within a dynamic environment. Retailers are replenished from regional warehouses and these are supplied by a central distribution entity. Transshipment at both regional warehouses and retailers levels is allowed. A mixed integer linear programming model is developed, where product demand at the retailers is assumed to be unknown. The problem consists of determining the optimal reorder policy by defining the new concept of robust retailer order, which minimizes the overall system cost, including ordering, holding in stock and in transit, transportation, transshipping and lost sales costs while guaranteeing service level. The proposed model is extended to address simultaneously uncertainty in both products demands and replenishment lead times. A case study based on a real retailer distribution chain is solved. Keywords: Distribution; inventory planning; mixed integer linear programming; uncertainty; scenario planning approach. On multi-state two separate minimal paths reliability problem with time and budget constraints   by Majid Forghani-Elahabad, Nezam Mahdavi-Amiri, Nelson Kagan Abstract: In a stochastic-flow network, a (d, T, b, P1, P2)-MP is a system state vector for which d units of flow can be transmitted through two separate minimal paths (SMPs) P1 and P2 simultaneously from a source node to a sink node satisfying time and budget limitations T and b, respectively. Problem of determining all the (d, T, b, P1, P2)-MPs, termed as the (d, T, b, P1, P2)-MP problem, has been attractive in reliability theory. Here, some new results are established for the problem. Using these results, a new algorithm is developed to find all the (d, T, b, P1, P2)-MPs, and its correctness is established. The algorithm is compared with a recently proposed one to show the practical efficiency of the algorithm. Keywords: Stochastic quickest path reliability problem; Transmission time; Budget constraint; Minimal paths (MPs); (d; T; b; P1; P2)-MP. On gH-differentiable harmonic invex fuzzy mappings and its applications   by Sunita Chand, Minakshi Parida Abstract: In this paper, we have introduced harmonic invex (H-invex) and harmonic incave (H-incave)fuzzy mappings by using the concept of gH-differentiability and many important results are obtained related to pseudoinvex (pseudoincave), quasiinvex (quasiincave), H-preinvex (H-preincave),η-monotone, η-dissipative, pseudoinvex monotone and pseudoincave dissipative fuzzy mappings. We have justified our results with suitable examples. Furthermore we have also applied gH-differentiable H-invex fuzzy mappings to study the KKT conditions for\r\nHarmonic invex fuzzy programming problem (HIFP), duality results and minmax problem. Keywords: Fuzzy optimization; H-invex (H-incave) fuzzy mappings; KKT conditions;\r\nDuality results; Minmax problem. An inventory system with Retrial demands, Multiple vacations and Two Supply Modes   by Anbazhagan Neelamegam, Kathiresan J Abstract: This paper analyzes a continuous review inventory system with single server, multiple vacations, Poisson demand, retrial demand, exponential distributed lead time and two supply modes for replenishment with one having a shorter lead time. To derive the stationary distribution of the system, we employ the Gavers method. After computing various system performance measures, some cost minimization numerical results are presented. Keywords: {Continuous review inventory system; Positive leadtime; Retrial demand; Multiple vacations; Two supply modes. A Co-operative Combined Defense Technique for Jamming Attack in MANET   by A. Jayanand Abstract: Mobile ad hoc networks (MANET) consist of continuously mobile nodes in the network. Due to this dynamic nature of the network, new nodes keep joining the network and some nodes exit the network every now and then. As a result, keeping track of every node in the network is not possible. So, malicious nodes like jammers can easily enter the network and affect the efficiency of the network. Hence, in this paper, we develop a co-operative combined defense technique for detecting jamming attacks in the MANET by determining the presence of jammers in the network. This is achieved by combining several important factors like Correlation coefficient, Carrier Sensing Time, Packet Delivery Ratio (PDR) and signal strength (SS). Then a trust model is derived for each node and updated based on these measured parameters. Each node collaboratively checks the updated trust values of a suspected node and detects the jamming attack. Simulation results show that the proposed technique improves the detection accuracy and packet delivery ratio. Keywords: Mobile ad hoc networks; Packet Delivery Ratio; signal strength; Jamming Attack. Steady State Analysis of Fluid Queues Driven by Birth Death Processes with Rational Rates   by Shruti Kapoor, Dharmaraja Selvamuthu Abstract: Birth death processes with rational birth death rates have been studied by Maki [9]. This paper analyzes the steady state behavior of a fluid queue driven by a finite birth death process with rational birth and death rates. Two specific models are considered and closed form solutions are obtained for the equilibrium distribution of the buffer occupancy by finding the explicit eigenvalues of the underlying tridiagonal matrix. Numerical illustration is presented for different values of the size of the state space of the background process and for different values of the parameter involved. Keywords: Fluid queue; stationary distribution; eigenvalues; tridiagonal matrices. Hierarchical MAC protocol With Adaptive Duty-cycle Adjustment Algorithm for Wireless Sensor Network   by C. Venkataramanan, S.M. Girirajkumar Abstract: In wireless sensor networks (WSNs), the existing routing algorithms causes increased energy utilization and minimizes the lifetime of the network. In order to conquer this problem, in this paper, an adaptive duty-cycle adjustment algorithm based on the traffic and channel condition is put forwarded. Initially, the node with higher weight value is chosen as cluster head, the value is calculated from residual energy and delay between the successive transmissions. After the cluster formation, the relay nodes are selected by their remaining battery energy and the channel state in the network for the data transmission. Based on the relay nodes found, the network traffic is controlled by the traffic adaptive duty-cycle. In this approach, the heads of each cluster collects traffic information from member nodes and computes appropriate duty cycle according to current traffic, and then the resulting duty cycle information conveyed to normal nodes. The node then executes the data transmission based on the duty cycle. Our results revealed that the proposed approach minimizes the energy utilization and enhances the network lifetime too. Keywords: wireless sensor networks; MAC protocol; adaptive duty-cycle. Systems Reliability Assessment Using Hesitant Fuzzy Set   by Akshay Kumar, S.B. Singh, Mangey Ram Abstract: The present study deals with fuzzy reliability evaluation series, parallel and linear (circular) consecutive k-out-of-n:F systems. Fuzzy reliability of series, parallel systems have been evaluated to help of hesitant fuzzy sets and triangular fuzzy number, whereas fuzzy reliability of linear (circular) consecutive k-out-of-n: F systems have been determined with the help of application of Weibull distribution and Markov process in comporting hesitant fuzzy sets and triangular fuzzy number. Numerical examples are also provided to demonstrate the effectiveness of the proposed approach. Keywords: Series system; Parallel system; Weibull distribution; Linear (circular)(k; n: F) system; Hesitant fuzzy set; Hesitant fuzzy weighted averaging operator. A MODIFIED GENERALIZED INVERSE METHOD FOR SOLVING GEOMETRIC PROGRAMMING PROBLEMS WITH EXTENDED DEGREES OF DIFFICULTIES (K is at least zero)   by Harrison O. Amuji, Fidelis I. Ugwuowo, Walford I. Chukwu, Peter. I. Uche Abstract: We have developed a new method of solving geometric programming problems with as many positive degrees of difficulties as possible. Geometric programming has no direct solution whenever its degrees of difficulties are greater than zero; this has hindered the development of geometric programming and discouraged so many researchers into the area. The indirect solution, which has been in existence, involves the conversion of geometric programming problems to linear programming, separable programming, augmented programming etc. These conversions make the beauty of geometric programming to be lost and also terminate the existence of geometric programming. The newly developed method (Modified generalized inverse method) consistently produces global optimal solutions; satisfies the orthogonality and normality conditions; optimal objective function; and produce optimal primal and dual decision variables which satisfy the optimal objective function. The method was applied on some positive degrees of difficulty geometric programming problems and the results compare to the results from existing methods. The method was validated by some proposition; corollary and lemma. With this breakthrough, geometric programming problems can be modeled and solved without restrictions. Keywords: Exponent matrix; Degree of difficulty; generalized inverse; Primal decision variables; Dual decision variables; Objective function. A multi-objective approach for locating temporary shelters under damage uncertainty   by Ashish Trivedi, Amol Singh Abstract: Every year, natural disasters such as earthquakes, hurricanes, landslides, etc. kill thousands of people and destroy habitats and assets worth millions-of-dollars. Choice of temporary shelter areas and subsequent relocation of homeless people play a crucial role in post-earthquake relief operations. This paper proposes a multi-objective location-relocation model based on goal programming approach considering uncertainties of damage to infrastructure due to earthquakes. The model considers multiple objectives of risk, number of sites, unmet demand & qualitative suitability of locations and generates solutions under different scenarios of damage. A numerical illustration is also presented to demonstrate the applicability of proposed approach in solving the decision problem. Keywords: Disaster; Goal programming; Humanitarian logistics; shelter site selection; uncertainty. Multi-criteria Approach for Platelet Inventory Management in Hospitals   by Suchithra Rajendran, A. Ravi Ravindran Abstract: In this paper, a multiple criteria approach is proposed for platelet inventory management in hospitals. It has been reported that about 20% of the total platelet collected is outdated due to the short shelf life of platelets and demand uncertainty. A multiple criteria mathematical programming (MCMP) model is developed to minimise platelet wastage, shortage, and procurement and holding cost. A case study is discussed by applying the model to the daily demand data of platelets at a New York hospital. The MCMP problem is solved using three MCMP techniques; preemptive goal programming (PGP), non-preemptive goal programming (NPGP) and weighted objective methods (WOM). Managerial implications of the optimal solutions are discussed. Sensitivity analysis is also performed to analyse the impact of goal priorities in the PGP model and weights in the NPGP model and WOM. Based on the policies obtained under PGP, NPGP and WOM methods, the hospital management can decide the most suitable inventory policy for implementation. Keywords: Platelet inventory management; multiple criteria mathematical programming; preemptive goal programming; non-preemptive goal programming; weighted objective methods; sensitivity analysis. A Comparative Analysis between LINMAP, Paired Comparison Method and Naturalistic Ranking in Different Data Display Contexts   by Hanane Taffahi, David Claudio Abstract: This article presents a comparative analysis between two widely used decision-making methods, LINMAP and Paired Comparison Method (PCM), using three different judging contexts. Decision makers ranked alternatives (for LINMAP) and criteria (for PCM) for contexts involving quantitative data only, qualitative data only, and a mix between the two. Attribute weights were calculated and final rankings of alternatives were deducted and compared to a naturalistic ranking of alternatives by the decision makers. LINMAP was found to be the closest match to a naturalistic decision-making. It was also found that incorporating qualitative data or a mixture between qualitative and quantitative data in multi-attribute decision-making problems was more consistent with the naturalistic ranking of alternatives. Keywords: LINMAP; Paired Comparison Method; Naturalistic Ranking; quantitative data; qualitative data; multi-attribute decision-making. Unreliable Server Retrial G-Queue with Bulk Arrival, Optional Additional Service and Delayed Repair   by Charan Jeet Singh, Sandeep Kaur, Madhu Jain Abstract: The retrial bulk arrival queue with unreliable server and negative customers is considered. On arrival of the group of customers, one of the customers gets the service immediately if the server is idle and other customers join the retrial orbit. There is a provision to opt additional service after completion of the essential service of the customers. The server may fail due to arrival of negative customers during any stage of the service. After completion of the service, the customer may again join the queue as a feedback customer to get another regular/optional service or depart from the system. The non-persistent (impatient) phenomenon also occurs because of the delayed in repair/ repair time of the failed server. By using the supplementary variable approach, various measures of queueing and reliability characteristics are analyzed. To facilitate the comparative study of the performance metrics of the system, the maximum entropy principle is used. The numerical results for various performance indices and optimal cost are obtained. Keywords: Unreliable server; Retrial queue; Non-persistent; Supplementary variable; Feedback; Optional service. A Review of Job shop Scheduling Problems in Multi-Factories   by Imen Chaouch, Olfa Belkahla, Khaled Ghedira Abstract: The Distributed Job shop Scheduling Problem (DJSP) deals with the assignment of jobs to factories geographically distributed and with determining a good operation schedule of each factory. It is one of the well-known NP-hard combinatorial optimization problem to solve optimally. In the last two decades, the problem has captured the interest of a number of researchers and therefore various methods have been employed to study this problem. In this paper, we first present an overview of pioneer studies conducted on solving Distributed Job shop Scheduling Problems and a classification of the employed techniques is given. Then, depth analysis of the outcome of existing literature is presented. Keywords: Distributed Scheduling; Job shop; Flexible Job shop; Optimization method; Survey. A graph theoretic-based approach to distribution network planning with routes interaction regarding the fix-charge transportation problem   by Babak H. Tabrizi, Masoud Rabbani Abstract: This paper aims to take distribution network planning problem into consideration, since a well-configured network can provide an appropriate platform for effective and efficient management of the set. The fix-charge transportation approach is addressed here to account for the problem. Hence, a non-linear mixed-integer programming model is proposed to minimize the configuration costs, in addition to the routes interaction consideration. Likewise, a graph theoretic-based methodology, i.e., the minimum spanning tree concept, is pursued by the Pr Keywords: Distribution network configuration; spanning tree; genetic algorithm; simulated annealing algorithm. Bank oligopoly competition analysis using a differential equations model   by Miltiadis Chalikias, Panagiota Lalou, Michalis Skordoulis, Perikles Papadopoulos, Stavros Fatouros Abstract: The purpose of this paper is to propose a model of differential equations that will be able to be applied in a bank oligopoly competition case. The differential equations model will be based on Lanchesters combat model, a well-known mathematical theory of war. Due to the fact that an oligopoly of four banks will be examined, the proposed model will consist of a 4x4 differential equations system. Many researchers have already concluded that mathematical theories of war models can be successfully applied to business cases as there are many similarities between the battle fields and the business competition. Since the proposed models predictions concern the deposits evolution, this model can contribute in the analysis of the competition between the four major banks in Greece. The statistical analyses carried out confirm the models good fit. Keywords: Operations research; Lanchester combat model; differential equations; oligopoly; bank competition; banking sector. Optimal Sourcing Policies for Single and Multiple Period Scenarios   by Shantanu Shankar Bagchi, A.K. Rao Abstract: Determining the optimum number of suppliers and the optimum quantities to order from each of them is a critical problem for any supply chain. The objective of this paper is to identify the optimal sourcing policy of a retailer for single and multi-period context when the firm can source its order to multiple suppliers along with a back-up supplier for emergency situations. The expected total profit is mathematically modeled for single and multi-period scenarios. The optimal sourcing policy is obtained by maximizing the expected total profit with respect to the order quantities. Closed form solution is obtained for uniformly distributed demand for both single and multi-period scenarios. It is observed that the multi-period solution is less sensitive compared to the single-period solution. Also it is found that it is optimal for the firm to lessen the amount of supplier diversification in case of planning for multiple periods. Keywords: Sourcing; Supplier yield; Stochastic model; Demand uncertainty; Supply uncertainty; Optimization. Genetic Algorithm for Quadratic Assignment Problems: Application of Taguchi Method for Optimization   by T.G. Pradeepmon, Vinay V. Panicker, R. Sridharan Abstract: Quadratic Assignment Problems (QAPs) are the hardest of combinatorial optimization problems, with some problems of sizes of the order of 30 still remaining unsolved optimally. Solving QAPs with exact optimization methods is cumbersome and hence, the use of non-conventional optimization methods is recommended. Genetic Algorithm (GA) being one of the most popular evolutionary algorithms is an appropriate choice for solving QAPs. The methods of operations used in GA influence the solution quality and thus, an optimal combination of parameters and operators are required for the efficient implementation of the algorithm. In this paper, the Taguchis design of experiments method is used to find the best parameter combination and the best performing combination of operations for GA. The GA thus obtained by incorporating the selected parameter values and operators is then used for solving the QAPs taken from the QAP Library. For many of the problems, it is found that the results obtained are within one percentage deviation from the best-known solutions. Keywords: Quadratic Assignment problem; Genetic Algorithms; Taguchi’s design of experiments method; optimization of operations and parameters. A Hybrid Approach of NSGA-II and TOPSIS for Minimizing Vibration and Surface Roughness in Machining process   by Zeelanbasha Noor Basha, Senthil Vellimalai, Mahesh Gopal Abstract: Increasing vibration amplitude during end milling process can seriously affect the life of end mills and reduces surface finish. Spindle and worktable vibration has a significant influence on surface quality of machined components. This paper confronts and investigates the effect of machining and geometrical parameters (spindle speed, feed rate, axial depth of cut, radial depth of cut and radial rake angle) on spindle and worktable vibration in terms of acceleration amplitude and surface roughness. Experiments were conducted on aluminium alloy 6061-T6 with High-Speed Steel (HSS) end mill cutter based on the Central Composite Design (CCD). Response Surface Methodology (RSM) was used to develop the predictive models and the adequacy of the models were verified using Analysis of Variance (ANOVA). Non-Dominated Sorting of Genetic Algorithm (NSGA-II) was adopted to solve the multi objective optimization problem and the optimized results were resulted with a set of Pareto-optimal solutions. The Multi Criteria Decision Making method (MCDM) such as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Analytical Hierarchy Process (AHP) were designed to rank the Pareto optimal solutions based on response of closeness coefficient values. The result shows that the average surface roughness can be minimized while the spindle and worktable vibrations are reduced in a simultaneous manner. Keywords: Aluminium Alloy; Decision making; End Milling; Machining; NSGA-II; Optimization; Prediction; TOPSIS.DOI: 10.1504/IJOR.2020.10008720  Routing vehicles through cross-docking facility for third party logistics service providers   by Birasnav M, Ramesh A, Kalaivanan S Abstract: This study focuses on a specialized Vehicle Routing Problem (VRP) to transport matchboxes from manufacturing companies to retailers through a cross-dock (cross-docking facility) operated by a third party logistics service provider. Three processes (unloading, consolidating, and loading) are carried out at a cross-dock for completely avoiding or keeping inventory for a very short time. The specialized VRP, addressed in this paper, consists of multiple suppliers (each supplier can produce different brands of products for any number of customers) and multiple customers (each customer can receive orders from any number of suppliers). A mixed integer linear programming model has been developed to solve this kind of NP-hard problem. The objective of this model is to minimize total cost incurred in picking up and transporting the matchboxes from the manufacturers to cross-dock, consolidating matchboxes at cross-dock, and in transporting and delivering the matchboxes to the customers. This study also proposes an effective heuristic procedure to solve the same problem and compares the solution obtained using the heuristic procedure to the optimal solution obtained using the exact method. The findings show that the heuristic method, proposed by us, generates near optimum solutions using significantly less computational time than the exact method. Keywords: Vehicle Routing; Cross-docking; NP-hard; Heuristic; Logistics service provider; Consolidating; Multiple Suppliers; Multiple Customers. Winsorize tree algorithm for handling outlier in classification problem.   by Chee Keong Ch'ng, Nor Idayu Mahat Abstract: Tree classification has been widely used nowadays for providing users supports in classification and prediction. Having outliers in a data set is inevitable, but ignoring the outliers may distort the size and accuracy of the tree as the outliers could affect a splitting point along the process of tree construction. This paper tackles the issue by proposing a winsorize tree algorithm that performs a process of detecting and handling the outliers while constructing a tree in all non-terminal nodes. Empirical results based on seven real data sets provide evidences that the proposed algorithm performs as good as or even better than the classical tree and pruned tree. Keywords: winsorize tree algorithm; gini index; error rate; classification; outlier; classification and regression tree (CART); winsorized tree. Location-Allocation Models for Healthcare Facilities with Long Term Demand   by Ruilin Ouyang, Tasnim Ibn Faiz, Md. Noor-E-Alam Abstract: Facility location decisions are long term commitments that manufacturing and service industries require to make in accordance with their vision statement, competitive strategies, and with the provisions for future uncertainty. Such decisions involve huge investments, and once the decisions have been executed, recourse options are very costly. Healthcare facility location and allocation decisions are of great importance due to their impact on accessibility to healthcare as well as direct and social cost of peoples well-being in a region. Healthcare facility location decisions that are optimal for current demand may become sub-optimal as demand distribution changes due to population growth and rapid urbanization. Therefore, future demand realizations should be incorporated in the decision making process to ensure long term optimality. The current study presents three mathematical models following grid-based location problem approach and take into account current and future demands in the decision making process. The decisions from the models are optimal long term healthcare facility location and patient allocation decisions for the current time and for a future time point. The first model provides the optimal locations for multiple types of facilities to be built at present and at the future time point and the corresponding allocations of patients to the nearest facilities. In the second model, instead of restricting patient allocation to the nearest facility, a relaxed allocation policy is considered where patients can go to facilities within allowable travel distance. The third model follows more relaxed allocation policy by allowing allocation of patients from one location to multiple facilities. Integer allocation variables are introduced and binary variables are discarded. Finally, the models are implemented with a standard modeling language AMPL and numerical instances were solved with CPLEX solver. Results show that all the models are capable of solving small to medium size problems. In terms of solution quality and computational time, the third model was found to be more suitable than the other two. The long term decision making approach presented in this study can be of great value for government and other organizations in making optimal decisions regarding healthcare or other service facilities. Keywords: Healthcare facility location; Grid-based location problem; Long term location decision; Integer programming. Improving recommendation quality by identifying more similar neighbours in a collaborative filtering mechanism   by Rahul Kumar, Pradip Bala, Shubhadeep Mukherjee Abstract: Recommender systems (RS) act as an information filtering technology to ease the decision-making process of online consumers. Of all the known recommendation techniques, collaborative filtering (CF) remains the most popular. CF mechanism is based on the principle of word-of-mouth communication between like-minded users who share similar historical rating preferences for a common set of items. Traditionally, only those like-minded or similar users of the given user are selected as neighbours who have rated the item under consideration. Resultantly, the similarity strength of neighbours deteriorates as the most similar users may not have rated that item. This paper proposes a new approach for neighbourhood formation by selecting more similar neighbours who have not necessarily rated the item under consideration. Owing to data sparsity, most of the selected neighbours have missing ratings which are predicted using a unique algorithm adopting item based regression. The efficacy of the proposed approach remains superior over existing methods. Keywords: collaborative filtering; recommender systems; similarity coefficient; true neighbours; prediction algorithm. Best A* Discovery For Multi Agents Planning   by Mohammed Chennoufi, Fatima Bendella, Maroua Bouzid Abstract: This paper proposes a new approach for multi-agent planning and decision support. The conventional algorithms such as Dijkstra, A* cannot solve complex problems with spatio-temporal constraints. So we are interested in developing a new strategy for the best path based on BDI agents for an emergency evacuation problem of a population crowd, besides the study of the macroscopic behavior emerging from simple interactions between agents by decreasing the evacuation time which is a challenge and a very complex task. Multi-agent systems are well suited to modeling such systems. The idea is to make a two-dimensional modeling of the environment as a Quadtree graph and an hybrid architecture: A* search from the node, where the individual is located to direct it to the best exit node while adding physiological factors to this search, a robust method for collision avoidance and decision support to help the agent will replace the initial destination with anew one. Our model is implemented and tested with java and Netlogo 5.2.1 platform. Keywords: Complex System; A*; Multi-agent systems; Crowd; Path; Decision Support; Planning; Evacuation; Simulation; Emergence. Evaluation of Ethanol Multimodal Transport Logistics: A Case in Brazil   by Henrique Correa, Peter Wanke, Andre Martins Abstract: This paper evaluates a large-scale ethanol multi-modal logistics system in Brazil. This system mainly involves ethanol logistics activities using pipelines and waterways to supply the Brazilian internal and export markets. A transshipment model is used for the treatment of logistic flows. A linear programming model was developed to determine the transshipment and replenishment flows from more than 400 ethanol plants to more than 70 terminals and distribution centers using various modes of transportation. Optimal results occur when pipeline and waterway systems reach full capacity by taking volume away from road transportation on long distances, suggesting that the use of these options has the potential to make the ethanol logistics in Brazil more efficient and competitive in the future. Keywords: Ethanol; Transshipment; Pipeline; Waterways; Linear Programming. A reverse logistics model for decaying items with variable production and remanufacturing incorporating learning effects   by Swati Sharma, S.R. Singh, Mohit Kumar Abstract: In order to meet environmental concerns/regulations, suppliers often endeavor to recover the residual value of their used products through remanufacturing. In this research article, an integrated production and remanufacturing inventory model for a single supplier and a single buyer is presented. There is one production and one remanufacturing cycle for the supplier while multiple batches are considered for the buyer. Demand rate for the supplier and buyer is taken a linearly increasing function of time. It is presumed that production, remanufacturing and returned rates are demand dependent and items deteriorate while they are kept in storage. This model also incorporates the effect of learning in ordering cost, holding cost, deteriorating cost and purchasing cost for the buyer as these costs reduce cycle by cycle due to learning effect from the previous cycle. The numerical examples, sensitively analysis and graphical illustrations are given to illustrate the model. Keywords: reverse logistics; inventory model; deterioration; variable production and remanufacturing; learning effects. Heuristics for disassembly lot sizing problem with lost sales   by Mustapha HROUGA Abstract: Disassembly is a major activity performed in treatment and recovery facilities and is the most important precedence of product and recovery part. It is defined as a systematic method of separating a product into its constituent parts and subassemblies. As economic activities and environmental pressures increase, the volume of product reverse flows are more and more important and costly. This paper focus on single-item disassembly lot sizing problem without and with lost sales, we propose an optimization approach to minimize the set-up costs and inventory costs in a first time and in second time we include lost sales costs. Compared to classical lot sizing problems with lost sales on a finite planning horizon, our problem has some specificities that require original optimization methods. To this end, we propose three most well-known heuristic approaches for the single-item disassembly lot sizing problem without and with lost sales: Silver Meal (SM), Part Period Balalncing (PPB) and Least Unit Cost (LUC). All three heuristics are myopic in the sense that they only consider the costs between two set-ups periods focus solely on the next demand and ignore costs associated with future demand and have outstanding results for classical lot sizing problem with returns which made then more easy to implement. The performance of the proposed solutions is compared with those obtained from a mathematical programming solver for small instances using different demands configurations (increasing, decreasing and variables) and planning horizon (10, 20 and 30 periods). Results show that the three heuristics used in the classical lot sizing problem can be also used to solve the disassembly lot sizing problem without and with lost sales, especially for small and medium instances. Results also show that: a) SM and PPB outperform LUC, b) increased variation in the demand quantity can lead to reduced cost, showing that certainty is more important than variation of the demands, and c) comparison between proposed heuristics and CPLEX, as an exact solution for small and medium size problems (since there is no effective dynamic programming method for the problem with lost sales), shows that we can trust the proposed heuristics as a solution methodology to solve disassembly lost sizing without and with lost sales for larger instances and more complex problems such as multi-level or capacitated disassembly lot sizing. Keywords: Disassembly planning; lot-sizing; reverse logistics; lost sales. Application of a genetic algorithm for multi-item inventory lot-sizing with supplier selection under quantity discount and lead time   by Sunan Klinmalee, Chirawat Woarawichai, Thanakorn Naenna Abstract: This study presents an application of genetic algorithm (GA) for solving the multi-item inventory lot-sizing problem with supplier selection under discounts and lead time constraints. A mixed-integer linear programming (MILP) model is developed for proposed problem. To solve the problem, a genetic algorithm (GA) with two additional operations is proposed for handling the effect of the problem size. An adaptor for adjusting a chromosome data before the evaluation process and a penalty step for deterring an infeasible solution are developed. Finally, numerical examples are generated to evaluate the performance of the proposed GA, and the comparison with MILP approach about the solution quality and time is presented. Keywords: genetic algorithm; inventory lot-sizing; supplier selection; lead time; quantity discount; mixed-integer programming. Hybrid BBO PSO based Extreme Learning Machine Neural Network Model for Mitigation of harmonic distortions in Micro Grids   by Gunasekaran Subramanian, Maheswar Rajagopal Abstract: Microgrid tends to be the cluster of some of the renewable energy sources like photovoltaic, wind, diesel engine, fuel cell and so on. The most important research area in the power distribution system side is the improvement in the quality of power delivered to the end users. This paper focuses on enhancing the power quality of the microgrid system at the distribution point. Here, in order to improve and deliver quality power, shunt active power filter is employed at the distribution side and the main aim of this paper is to design an appropriate controller that achieves a better compensation for the considered shunt active power filter. It is to be noted that the compensation methodology is dependent on the regulation process of the DC-link capacitor voltage. Traditionally, this regulation process is carried out employing a closed loop proportional-integral (PI) controller. In this paper, a hybrid Biogeography Based Optimization (BBO) Particle Swarm Optimization (PSO) based Extreme Learning Machine (ELM) neural network model is proposed to design the compensation for the shunt active power filter as well to mitigate the harmonics so that effective power gets delivered through the grid. The proposed Hybrid BBO-PSO based ELM as applied for the considered microgrid system is compared with the other methods available in the literature to prove its validity. Simulation results shows that the proposed hybrid controller achieves better solutions for compensating the shunt active power filter for harmonic mitigation in microgrids than the other methods. Keywords: Microgrid – Shunt active power filter – Power Quality – Harmonic Mitigation – Biogeography Based Optimization – Particle Swarm Optimization – Extreme Learning Machine Neural Networks. Interference reduction using Particle Swarm Optimization in MIMO-WCDMA Multicellular Networks   by Mohan N. Abstract: In this paper, Particle Swarm Optimization (PSO) algorithm based interference reduction is proposed in Multiple Input Multiple Output (MIMO) using wide-band code division multiple access (WCDMA). During transmission MIMO network may get interfered by some interference such as co channel interference and adjacent channel interference. To reduce these interferences many algorithms have been proposed in previous research. Further improve the performance of the MIMO-WCDMA network and reduce the bit error rate (BER) an optimized algorithm is PROPOSED. Simulation results of this paper show that bit error rate (BER) is reduced and also throughput of the network also improved. Keywords: Particle Swarm Optimization (PSO); Multiple Input Multiple Output (MIMO); WCDMA; BER. Blocking Probability based Admission Control Technique for QoS Provisioning in WDM networks   by M.R. Senkumar, K. Chitra Abstract: In this paper, we have proposed a Blocking Probability Based Admission Control Technique for QoS Provisioning on in WDM networks, for this, we estimate the blocking probability for an arriving connection request. The probability that there is at least one free wavelength at the specified book-ahead time that remains idle for the whole connection duration. Next to this an admission control scheme used in each group for deterministic QoS provisioning. The admission control scheme has its root from network calculus which can derive deterministic bounds on throughput and delay rather than statistical averages. Along with the delay metric, the blocking probability is also considered as the main constraints for admission control. The scheme allocates the aggregate token bucket for each class of traffic based on its bandwidth share. Keywords: WDM networks;QoS;Blocking probability. A suggested method for solving capacitated location problems under fuzzy environment   by Maged Iskander Abstract: In this paper, a new approach for solving fuzzy capacitated location problems is proposed. Both the capacity and the demand constraints are considered fuzzy while the objective function is not. The max-min approach is utilized within the proposed method. A membership function is defined for the non-fuzzy objective function to convert it to a fuzzy one. The α-cut is employed for the membership functions. The models which are in the form of mixed zero-one nonlinear programs are transformed to their equivalent linear ones. Four mixed zero-one linear programs are required to be sequentially solved. The solution of the fourth program represents the ultimate optimal solution of the problem. The suggested approach is illustrated by a numerical example. Keywords: fuzzy programming; fuzzy capacitated location problem; max-min approach; Chang’s linearization approach; mixed zero-one programs. An Analysis of Korean Bank Performance Using Chance-Constrained Data Envelopment Analysis   by Yong Joo Lee, Seong-Jong Joo, TaeWon Hwang Abstract: For measuring the performance of firms using data envelopment analysis (DEA), many studies assume that inputs and outputs are deterministic. For example, key indicators for financial institutes such as assets, deposits, number of employees, and profits vary over time. Nonetheless, researchers take snapshots of these numbers and analyze them for performance measurement and benchmarking. Similarly, it is not an exception for the studies with DEA for Korean financial institutes. We allow inputs and/or outputs to be stochastic and analyze the comparative performance of Korean banks. We found that large or top five banks were inconsistent sensitivity on the variability of inputs and/or outputs across models. The contributions of our study include demonstrating DEA analysis using stochastic inputs and outputs for the Korean banks and providing realistic insights to the managers of the banks. Keywords: Performance measurement; benchmarking; data envelopment analysis; stochastic variables; Korean banks; chance constrained DEA. ABC Algorithm for Estimation of Dynamic Parameters in Radial Power System Transfer path   by Jeha J., S. Charles Raja Abstract: In the paper, an efficient technique is utilized for improving the dynamic performance of interconnected power system. Here, the artificial bee colony algorithm (ABC) is used to predict the stability of the power system and is evaluated the aggregated machine reactance and inertias in the transfer path. The proposed method is used for estimating the dynamic parameters of the aggregated machines for each area utilizing the amplitudes of voltage oscillations measured at any three intermediate points on the transfer path. The two-machine reduced model is used to represent the inter area dynamics of a radial, two-area power system with intermediate dynamic voltage control. Two types of voltage control equipment are considered, namely, a static Var compensator (SVC), and a Thyristor Controlled Series Capacitor (TCSC). The proposed method focuses on transfer path which is utilized the TCSC for including the purpose of voltage support and reducing the disturbance in the system. Here, the proposed methods employ bus voltage phasor data at several buses including the voltage control bus, and the line currents on the power transfer path. Here, the three phase fault is applied in the power system. Based on the estimation, the dynamics of the power system is improved and the proposed strategy is utilized for improving the overall dynamic security. The proposed technique is implemented in MATLAB/Simulink working platform and the output performance is evaluated & compared with the existing methods such as without facts devices, SVC based controller and (Genetic Algorithm) GA based TCSC controller respectively. Keywords: Dynamic parameters; voltage; TCSC; SVC; reactance; inertia; ABC and GA.DOI: 10.1504/IJOR.2020.10011648  A continuous review policy based on the Stock Diffusion Theory: Analysis and insights via Monte-Carlo simulation   by Francesco Zammori Abstract: The Stock Diffusion Theory (SDT) is an innovative model for inventory management, which can be effectively applied even in case of heteroscedastic demand, evolving both in mean and variance. To operate, the SDT requires, as input, the trend of the mean μ(t) and that of the variance σ^2 (t) of the demand. Yet, estimating these functions may be challenging and so our goal is to assess the applicability of the SDT at the operational level. To this aim, we used the SDT to formulate a continuous review policy, characterized by a dynamic reorder level and, next, we introduced two practical ways to estimate μ(t) and σ^2 (t). Lastly, numerical Monte-Carlo simulations were used to assess the performances of the model, with respect to standard continuous review policies taken as benchmark. Obtained outcomes confirm the superiority of the SDT and its applicability in most practical cases. Keywords: Continuous review policy; Inventory management; Monte-Carlo Simulation; Stock Diffusion Theory. Adaptive Technique for Transient Stability Constraints Optimal Power Flow   by V. Manjula, A. Mahabub Basha Abstract: This document explains about an adaptive method for optimal power flow (OPF) of the power system, which is depending on the transient constancy restraints. The adaptive method is the mixture of both Cuckoo Search (CS) algorithm and Artificial Neural Network (ANN). The innovative anticipated adaptive method is extremely flexible in nonlinear loads, suitable for user interface and logical reasoning, and allowing controlling formats. In the predefined generator, the CS algorithm optimizes the generator arrangements by the load demand. The foremost intention of the CS algorithm is to reduce the fuel cost and emission cost. The obtainable ANN method is mainly used to develop the levy flight searching activities of the CS algorithm. The levy flight parameters are generally used to meet of the requirements the ANN, which envisage the precise consequences at the testing time. The anticipated adaptive method is executed in the MATLAB/Simulink platform and the efficiency of the anticipated procedure is investigated by the comparison analysis. Keywords: Optimal power flow; CS algorithm; Artificial neural networks; Cost minimization; Power loss reduction; Synchronous generator. Prioritizing Critical Failure Factors for the Adoption of ERP System using TOPSIS Method   by Santosh Kumar Yadav, Dennis Joseph Abstract: Enterprise resource planning (ERP) applications are complex and difficult to implement. Even after implementation many ERP projects are not used or adopted by employees. Organizations are struggling to convince and motivate employees to adapt smoothly to them. Several personal, managerial and organizational issues contribute to successful adoption. This research paper attempts to identify potential issues that lead to failures in the adoption of ERP systems in enterprises. Earlier studies have identified different contributing issues to the failure of ERP systems. A Questionnaire was developed around these significant influencing issues reported in literature and industry people mostly senior managers having good experience with ERP systems were asked to rate the importance of these factors. TOPSIS method was applied to rank the factors based on their importance in the failure of ERP systems. From the results, it is found that poor top management support and poor quality of testing were the two most important critical failure factors for ERP adoption. While implementing ERP systems, an organization has to give importance to these failure factors based on this rank to ensure ERP implementation success. Keywords: Enterprise systems; ERP; ERP failure factors; ERP adoption; TOPSIS. Evaluation and designing reverse logistics for risk-neutral and risk-seeking decision makers   by Aida Nazari Gooran, Hamed Rafiei, Masoud Rabbani Abstract: Designing appropriate supply chain would provide numerous valuable feedbacks for the whole chain, since using returned products instead of reproducing them, is a more appropriate response to the environmental concerns on the one hand which provides benefit and financial savings for the chains on the other hand. Therefore, this paper presents a three-objective function mathematical model to maximize financial savings and quantities of returned products to the chain and minimize total costs in terms of uncertainty and risk that derives from reverse logistics nature. Finally, the developed model was solved by Monte Carlo simulation and genetic algorithm along with proper risk measures for risk-neutral and risk-seeking decision makers. The results indicated financial savings are one of the best objective functions in order to show superiority of reverse logistics network. As another result, it was pointed out that profitability of the chain increases because of delivering return products before their scrap-life. Keywords: Reverse logistics; Uncertainty; Risk; Risk measures; Genetic algorithms; Monte Carlo simulation. Economic ordering policy for deteriorating items with inflation induced time dependent demand under infinite time horizon   by GEETHA KRITHIVASAN, UDAYAKUMAR RAMASAMY Abstract: This article deals with an Economic Order Quantity (EOQ) model for deteriorating items in which the demand is considered to be inflation induced time dependent under infinite planning horizon. Here, we have considered two different models, that is, shortages are not permitted in model-I and shortages are permitted with partial backlogging in model-II. The salvage value associated with the deteriorated units is also considered. The objective of this work is to minimize the total inventory cost and to find the optimal length of replenishment and the optimal order quantity. Numerical examples given illustrate the solution procedure. Comparative study between the two developed models is carried out. The insights obtained from managerial point of view are discussed in detail with the aid of sensitivity analysis with respect to major parameters of the inventory system. Keywords: Inventory;Deterioration;Inflation;Salvage value;Shortage;. Fuzzy Logic Based Multi Level Shunt Active Power Filter for Harmonic Reduction   by Elango Sundaram, Subramanian R, Manikandan V, Ramakrishnan K Abstract: - In this paper, using a three level diode clamped multilevel inverter and DC capacitor, a shunt active power filter (SAPF) is implemented to mitigate the supply current harmonics and compensate reactive power drawn from nonlinear load. The advantage of using three level inverter paves way to reduced harmonic distortion and switching losses. Fuzzy logic control and unit sine vector control are proposed in this paper for generating reference current for the SAPF. The advantage of fuzzy control is that it is based on a linguistic description and does not require a mathematical model of the system. The implementation of Fuzzy Logic Control (FLC) algorithm is executed using MATLAB fuzzy logic tool box. The proposed pulse width modulation (PWM) method produces the switching signals to the inverter from the sampled reference phase voltage magnitudes as in the case of conventional space vector PWM (SVPWM). The simulation results illustrate that the proposed three level SAPF with low harmonic content in supply current and in phase with the line voltage. The simulation results are validated with prototype model for demonstrating the effectiveness of the system. Keywords: Fuzzy logic; active filters; total harmonic distortion; pulse width modulation; reactive power. Constrained Project Scheduling Problem: A Survey of Recent Investigations   by Mohamed Abdelbaset, Asmaa Atef, Abdelnasser Hussien Abstract: Scheduling and managing projects are very important topics in project management science. Constrained resources project scheduling problem CRPSP is a problem of the purpose of allocating the available resources to specific tasks or activities for achieving specific objectives or purposes such as minimizing the makespan or time of the projects, minimizing the execution cost of the project, or any other specific objective or more than one objective at the same time (multi-objectives resource constrained project scheduling problems). Optimizing constrained resources project scheduling CRPSP is considered as a problem structure of deterministic nature. This structure case is an extension to the critical path method and with the resource usability constraints. Seeking for constrained resource scheduling procedures and scenarios is a very good researched domain considering that finding feasible scheduling plan or procedure under uncertainty conditions has been considered as a hot area for the recent research years and are of harm needs for the researchers' interest. This paper introduces a survey for procedure scenarios, techniques, and models that are considered the main context history of CRPSP and Multi-Mode Constrained Resource project scheduling problems MMCRPSP and classified based on research work principles itself. It aims to exhibits, highlights, and update the recent CRPSP surveys. The current state of art for recent researches is evaluated and the potential research directions and orientations are pointed. Also a new framework is proposed for the researchers of interest for this domain of research. Keywords: Constrained Resources Project Scheduling Problem - Multi-Mode Constrained Resource Projects – Exact methods – Heuristic methods – Meta-heuristic methods. High-level Stochastic Project Cost and Duration Planning Methodology Integrating Earned Duration, Schedule and Value, Criticality, Cruciality and Downside Risk Metrics   by David A. Wood Abstract: A high-level methodology is described to integrate deterministic and stochastic calculations of project networks with parallel pathways of work items. It provides the systematic integration of earned value, earned schedule and earned duration metrics and derivative to-completion forecasts of project cost and duration with stochastically-derived quantitative measures of criticality, cruciality, uncertainty and downside risk measures at project, work item and budget levels. A project network consisting of up to about fifty high-level project work items (rather than hundreds of activities) is evaluated applying critical path analysis using a matrix template that derives the fraction of the project completed at regular intervals (e.g. 2% to 5%) along a baseline planned project schedule the work-progress-breakdown diagram. This matrix is evaluated for each deterministic and stochastic case providing the key information to derive a spectrum earned value metrics, and to quantify uncertainty, down-side risk and criticality at the work-item, pathway and project levels. Keywords: project cost duration simulation; stochastic earned value duration metrics; probabilistic project network critical path; duration performance index DPI; project versus work-item criticality cruciality; quantified project risk uncertainty; project work-progress-breakdown diagrams. Sustainable Partner Selection: An Integrated AHP-TOPSIS Approach   by Ramanjan Bhattacharya, Rakesh Raut, Bhaskar. Gardas, Sachin Kamble Abstract: The selection of an efficient partner for any organization improves its overall performance. In the present research for the selection of an efficient, sustainable partner forty-nine selection criteria were identified through the exhaustive literature review, and by applying the Delphi technique, the evaluation criteria was reduced to sixteen. Later, analytic hierarchy process (AHP) was employed for calculating the relative weights of the shortlisting criteria. Then, the technique for order preference by similarity to ideal solution (TOPSIS) methodology was used for ranking the partners. The findings of the AHP approach revealed that cost (includes environmental cost)/price (C8), environmental competencies (concern for environment) (C15), and human resource management and human rights issues (C9) are the top three significant selection criteria and the results of TOPSIS highlighted that partner B is the best partner amongst the three identified partners. The developed model is intended to guide the decision and policy makers in the identification of the significance or importance of selection criteria, and for formulating the strategies or policies for the selection of efficient partners. Keywords: partner selection; multi-criteria decision making (MCDM); AHP; TOPSIS; textile industry. Managing unreliability in automotive supply networks - an extension of the joint economic lot size model   by Tim Gruchmann Abstract: Within assembly network supply chains, supply disruptions can occur on every supplier-buyer link. Managing this network unreliability can help to reduce schedule instability and increases the overall efficiency of the supply chain accordingly. In this line, a stylized assembly network supply chain model is proposed with two suppliers and a single buyer using the joint economic lot sizing approach. This supply network can be disrupted by a shortage occurring at one of the two suppliers due to random machine breakdowns, which consequently creates dependent requirements variations affecting both the buyer and the entire network. First, the basic joint economic lot sizing model is extended by the said schedule instability. Second, a solution approach is presented concerning the determination of optimal lot sizes, the investment into the reliability of the supply network as well as the determination of safety stocks. Furthermore, the sensitivity of relevant model parameters is investigated by means of a numerical example. Managerial implications are accordingly derived focusing on the reliability of the supply network members and internal incentive structures. Keywords: Schedule instability; Automotive supply networks; Joint economic lot sizing; Supply unreliability; Safety stocks. A deterministic production inventory model with defective items, imperfect rework process and shortages backordered   by Harun Öztürk Abstract: The basic assumption of the conventional inventory models is that all items produced are of perfect quality. In practice, some defective items are produced due to process deterioration or other factors. This paper develops a mathematical model for an imperfect production inventory system. It is assumed that the defective items produced in the regular production process consist of scrap, imperfect quality and reworkable items. The rework process is accomplished immediately when the regular production process ends, and the rework process produces scrap, imperfect quality and as-good-as perfect items. A numerical example is provided to illustrate the developed model, and a sensitivity analysis is carried out. It was found that producing scrap and imperfect quality items through the reworking is crucial, since this assumption effects optimal policy. Managerial insights are also presented based on the numerical examples. Keywords: inventory management; production planning; screening; defective items; imperfect rework process; shortages. A push strategy optimization model for a marine shrimp farming supply chain network   by Chaimongkol Limpianchob, Masahiro Sasabe, Shoji Kasahara Abstract: Marine shrimp farming operations in Southeast Asia are still traditional and need to be improved in efficiency. In this paper, we first model a marine shrimp supply chain network, which consists of suppliers, farms, distribution centres, traders, and consumers. We also develop a mixed-integer linear programming under the push strategy framework in order to maximize the farmers profit. Through a sensitivity analysis, we examine how the increase in costs affects the profits. The computational results are presented to demonstrate the feasibility of a real case of smart marine shrimp farming. Keywords: push strategy; supply chain network; mixed-integer linear programming; marine shrimp farming; giant freshwater prawns. Cost optimization and maximum entropy analysis of a bulk queueing system with breakdown, controlled arrival and multiple vacations   by Nithya R P, Haridass M Abstract: This article analyses a single server batch arrival general bulk service queueing system with multiple vacations, controlled arrival of batches and breakdown. The service is done in bulk with a minimum of a customers and a maximum of b customers. The server is assigned for secondary jobs (vacations) repeatedly when the number of customers is inadequate to process. However, all arrivals are not considered for service at all times. During the service period, the arrivals are accepted with a probability α, whereas, during the vacation period, the arrivals are accepted with a probability β. During a batch service, if the server breaks down with probability π, the service for the particular batch is processed without interruption. Upon completion of batch service, the renovation of service station will be considered and during renovation, the arrivals are accepted with probability γ. The probability generating function for the queue size at an arbitrary time epoch, for the proposed queueing model is derived. Various performance measures like expected queue length, expected waiting time, probability that the server is on vacation, probability that the server is busy, expected length of busy and idle period are obtained. A few particular cases are discussed to justify the result obtained. Maximum entropy principle is used to determine the solution for steady state probability distribution of queue size and expected waiting time in the queue. A comparative analysis of the results obtained and the analytical results of the proposed model is carried out. The final analysis is validated through numerical illustration. The cost model is also developed to optimize the cost and analyze the utilization of idle period. The findings of this research demonstrate that, for stochastic modelling of complex queueing systems, maximum entropy principle provides an easy approach to determine the unknown probability distributions subject to the mean value of constraints. Moreover, it is a feasible method which can be readily used in practice for approximating the analytical solution. Keywords: Bulk arrival; batch service; multiple vacations; breakdown; controlled arrival; maximum entropy principle. Pricing and cooperative advertising decisions in a two-echelon dual-channel supply chain   by Arash Apornak, ABBAS Keramati Abstract: Developments of e-commerce lead manufacturers and retailers to open direct online channel versus traditional channel in the market. In this paper we consider a supply chain consisting of a manufacturer and a retailer evaluate the impact of price schemes and cooperative advertising mechanisms on dual-channel supply chain competition in traditional and direct online channels as its setting by using Nash equilibrium and cooperative game then find the optima value of each decision variable of the study under preferred scenarios, According to the results the value of decision variables in traditional channel is more than direct online channel in both scenario and also in profit improvement part the analyses shows both channel is sensitive to demand, The results of this study can help managers to consider the interplay between the upstream and downstream entities of a dual channel. Keywords: Pricing; Cooperative advertising; Nash Equilibrium; Cooperative game; two echelon supply chain. Optimization of multi-plant capacitated lot-sizing problems in an integrated supply chain network using calibrated metaheuristic algorithms   by Maryam Mohammadi, Siti Nurmaya Musa, Mohd Bin Omar Abstract: In this paper, a mathematical model for a multi-item multi-period capacitated lot-sizing problem in an integrated supply chain network composed of multiple suppliers, plants and distribution centers is developed. The combinations of several functions such as purchasing, production, storage, backordering and transportation are considered. The objective is to simultaneously determine the optimal raw material order quantity, production and inventory levels, and the transportation amount, so that the demand can be satisfied with the lowest possible cost. Transfer decisions between plants are made when demand at a plant can be fulfilled by other production sites to cope with the under-capacity and stock-out problems of that plant. Since the proposed model is NP-hard, a genetic algorithm is used to solve the model. To validate the results, particle swarm optimization and imperialist competitive algorithm are applied to solve the model as well. The results show that genetic algorithm offers better solution compared to other algorithms. Keywords: capacitated lot-sizing; multi-plant; production and distribution planning; integrated supply chain; optimization; metaheuristic algorithms; genetic algorithm; particle swarm optimization; imperialist competitive algorithm. Location of Depots and Allocation of Buses to Depots in Urban Road Transport Organizations: A Mathematical Model and Greedy Heuristic Algorithm   by M. Mathirajan Mathi, P. Suba, Ramakrishnan Ramanathan Abstract: Optimizing the cost of operations is one of the major issues in any Urban Road Transport Organizations (URTOs). In this study a decision problem on location of depots (adding new locations and removing existing ones) and allocation of buses to depots is considered. The problem is solved for the case of Bangalore Metropolitan Transport Corporation (BMTC), a major URTO in Karnataka, India. The main focus of this research is to provide analytic methods to minimize the cost of operations comprising (a) dead-kilometre cost, (b) fixed cost associated with introducing new depots, and (c) salvage value due to closing the depots. To do so, a (0-1) mixed Integer Liner Programming (MILP) model is proposed and its workability is demonstrated. In addition to the proposed (0-1) MILP model, a simple greedy heuristic algorithm is also proposed. A computational experiment is developed to understand the performance efficiency of the proposed greedy heuristic algorithm in comparison with the optimal solution. From the average and worst case analyses of the performance evaluation, it is observed that the proposed greedy heuristic algorithm provides near-optimal solution (that is on an average the loss of optimality is less than 0.2 percent). The (0-1) MILP model or the efficient greedy heuristic algorithm proposed in this study can be used to help make better decisions on location of depots and allocation of buses to depots of URTOs in general. Keywords: Location of Depots; Allocation of Buses to Depots; Dead-Kilometre Costs; Salvage Value; MILP model; Greedy Heuristic Algorithm. Multi-objective simulation optimisation on discrete sets: a literature review   by Moonyoung Yoon, James Bekker Abstract: Simulation optimisation is an interesting and fast-growing research field fostered by advances in computer technology and increased computing power. These advances have made it possible to solve complex stochastic optimisation problems using simulation. Most simulation optimisation studies focus on single-objective simulation optimisation (SOSO), and multi-objective simulation optimisation (MOSO) has only recently drawn attention. This paper provides an overview of recent studies on discrete MOSO problems. We surveyed various MOSO algorithms and classified them, based on 1) the size of the feasible solution space, and 2) the method of dealing with the multiple objectives. For the latter, we identified three categories, namely scalarisation methods, the constraint approach, and the Pareto approach. MOSO algorithms in each category are discussed in some detail.rnWe conclude the paper by discussing some related issues in MOSO, which include noise handling techniques and the issue of exploration versus exploitation.rn Keywords: simulation; optimisation; multi-objective; ranking; selection. Rotary Heuristic for Uncapacitated Continuous Location-Allocation Problems   by M.D.H. Gamal Abstract: This paper proposes a constructive heuristic method to solve location-allocation problems. Specifically, we consider the problem of locating m new facilities in a continuous region such that the sum of the weighted distances from the new facilities to n existing facilities is minimized. The distance is measured using the Euclidean-distance metric. This simple technique shows that the solution found is encouraging for the case where the number of users is much larger than the number of facilities to be located. Keywords: facility location; heuristic; location-allocation. A modified column generation algorithm for scheduling problem of reentrant hybrid flow shops with queue constraints   by Bing-Hai Zhou, Ke Wang Abstract: To effectively enhance the production efficiency of multi-reentrant workshop, the queue constraint is taken into account where products are processed layer by layer, and then a scheduling method of reentrant hybrid flow shops based on column generation algorithm is proposed. Firstly, a two-stage scheduling model of reentrant hybrid flow shops is described with parallel machine of single item processing at the first stage and batch processing machine at the second stage and then a mathematical programming model is built with an objective of minimizing the total completion time. A column generation algorithm is developed by decomposing the scheduling problem into main problem and job-level sub-problem. Dynamic programming with multiple decision-making is designed to solve each sub-problem and the newly added column is combined to main problem. Further, the adaptive accelerating strategy is applied to effectively improve the algorithm convergence. In the process of generating integral solutions by using branch-and-bound method, the column pool is built and the neighborhood mutation method is employed. Finally, numerical experiments in different problem scales are carried out to analyze the proposed algorithm. Results verify the validness and feasibility of the proposed algorithm. Keywords: queue; reentrant; column generation; batch processing; dynamic programming. Economic Allocation of Farm Land for Commercial Crops-A Case Study in Kasargod Region of India   by Sunith Hebbar, Raveena Suvarna Abstract: Economic allocation of land, is an important activity in agricultural planning. Due to the changing prices of crops in market, its vital for a farmer to appropriately allocate the land for the various crops to maximize the income. Therefore, this study focus on allocation of land for commercial crops, namely arecanut, pepper, coconut and rubber. Initially, Linear Programming technique was applied to determine the optimum crop mix. The results of which is then compared with the traditional method adopted by the farmer. A sensitivity analysis was then performed to determine the optimal capital requirement. Later on to predict the behaviour of the income on a long run a SD model was developed. The factors like market price, cost of crops and weather conditions on yield were considered. The simulation results predicted that by 2030, the income will rise by 59% than the current condition if the suggested crop-mix is adopted. Keywords: Commercial crops; Linear Programming Model; Optimization of Crops; System Dynamics. Multi-Objective Production Planning Problem: A Case Study for Optimal Production   by Ahteshamul Haq, Srikant Gupta, Murshid Kamal, Irfan Ali Abstract: In this paper, we have formulated a multi-objective production planning model for a hardware firm. This firm produces different types of hardware locks and other items in their production run. The objectives of the firm are to minimize the production cost, minimize the inventory holding cost and maximize the net profit subject to the set of realistic constraints. The production planning problem of a similar type in the past formulated under the certain environment where the input information precisely known to the decision maker (DM). However, in most of the situations, the input information is not precisely known. In such situations, fuzzy set theory plays a vital role in modelling of the problem where the input data has some vagueness.The proposed model of production planning also been formulated under fuzzy environment. Both triangular and trapezoidal fuzzy numbers used to present the vagueness in the input information. The equivalent crisp form of the fuzzy model obtained by two different defuzzification approaches namely ranking function and αcut approach. Henceforth, the formulated models under the certain and fuzzy environment have been solved by the fuzzy goal programming approach. Keywords: Production Planning Problem; Multi-objective Optimization; Fuzzy Goal Programming; Fuzzy Set Theory. A hybrid GRASP for solving the bi-objective orienteering problem   by Hasnaa Rezki, Brahim Aghezzaf Abstract: This paper focuses on the bi-objective orienteering problem (BOOP) that arises in the tourist routes design problem in cities. In this multi-objective extension of the well-known orienteering problem (OP), each point of interest has different profits, which could reflect the multiple preferences of tourists. The aim is to find routes, limited in travel time, that visit some points of interest and provide the maximum of the different total collected profits. In order to determine an effective approximation of the Pareto optimal solutions, we propose a hybrid Greedy Randomized Adaptive Search Procedure (GRASP) in which a General Variable Neighborhood Search (GVNS) is used as an improvement phase. To evaluate the performance of the proposed approach compared to the Pareto Variable Neighborhood Search (P-VNS) technique, we have used the test instances and the results provided by the P-VNS taken from the literature. Computational results reveal that the hybrid GRASP algorithm generates better approximations of Pareto-optimal solutions compared to the P-VNS method. Keywords: Bi-objective orienteering problem; GRASP; GVNS; Hybrid; Pareto-optimal solutions. Special Issue on: Advances in Operations Research A Postponed Inventory System with Modified M Vacation Policy   by Padmavathi I, Sivakumar B Abstract: In this article, we analyse a postponed inventory system with a single server under modified M vacation policy, where the server can take atmost M inactive mode. We assume the demand process follows a Markovian Arrival Process and (s, S) ordering policy with exponential lead time. During the inactive mode, the server can be idle or go on vacation, which occurs due to the depletion of inventory. In every inactive mode, server avails an inactive idle period first followed by a vacation period. Inactive idle period and vacation period follow independent phase type distribution. The demand that arrives during the server inactive mode enters the pool of infinite size. The server selects a demand one by one on FCFS rule from the pool, as long as the inventory level is greater than the reorder point and inter selection time follows exponential distribution. A quasi birth and death process is formulated to analyse the system and solved by using the matrix-geometric method. We explicit some system performance measures on the steady state and some illustrative examples are discussed numerically. Keywords: Postponed inventory system; (s; S) ordering policy; modified vacation policy; Matrix-geometric method. Dynamic Analysis to Set Idle Time between jobs on a Single Machine   by Senthilvel A N, Umamaheswari S, Arumugam C Abstract: Scheduling problems are common phenomena in everyday life. Ordering of jobs or tasks to satisfy the constraint determines a schedule. The problem considered here is to find the optimal schedule so as to minimize the earliness and tardiness penalties. This paper proposes a technique to insert the idle time as tight as possible while meeting due date. The penalty, through the insertion of the idle time, is minimized on its own upto the point where no further minimization is achieved. The proposed algorithm gives rise to the set of upper and lower bounds on the objective function value of randomly generated problem set. The proposed algorithm partitions the set of jobs into subsets. Each subset can be scheduled in parallel and grouped later. To prove the effectiveness of the algorithm, 400 sets of different sizes ranging from 15 Jobs to 100 Jobs are solved. The proposed method can be used as a benchmark for future approaches in the area of specific due date scheduling. Keywords: Scheduling Algorithm; Job Sequencing; NP Class; Heuristic approach; Idle Time; Global Optimization.