International Journal of Operational Research (135 papers in press)
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.
Analysis of infinite buffer general bulk service queue with state dependent balking
by Gopal Kumar Gupta, Anuradha Banerjee
Abstract: This paper investigates the effect of impatient phenomena of the arriving customers in a bulk service queue, where inputs are flowing into the system according to the Poisson process and are served in groups according to the `general bulk service' (GBS) rule. On arrival, a customer decides whether to join or balk the system, based on the observation of the system size and status of the server, i.e., whether server is busy or idle. The steady state joint probability distributions of the number of customers in the queue as well as with the server is obtained by using the probability generating function method, which is based on the roots of the characteristic equations formed using probability generating function for steady state joint probabilities. Finally, various performance measures, such as, average queue length, average waiting time, probability that the server is busy, average queue length when server is busy, etc., have been obtained. The paper ends with several numerical discussions to demonstrate the effect of certain model parameters on the key performance measures.
Keywords: Balking; General bulk service rule; Joint probability distribution; Probability generating function; Poisson Queue.
Short-term operating room scheduling: a parallel machine under resource constraints problem
by Mohamed Amine ABDELJAOUED, Zied BAHROUN, Nour El Houda SAADANI
Abstract: The paper tackles the daily scheduling of surgical operations in an operating theatre. The considered operating rooms are identical and the set of operations is subdivided into groups, each of them performed by a single surgeon. The objective is to minimize the ending time of the last completed operation (makespan). We assume that the planning phase, which consists of determining the operations to be scheduled each day and assigning them to surgeons that are available on that day, is already fixed, and we only focus on solving the daily scheduling phase. To the best of our knowledge, few studies specifically focus on very short-term decisions (one-day horizon) in operating room management. The problem is NP-hard and is part of the parallel machine scheduling under resource constraints.
We provide two mathematical models and compare their performance. The first is based on parallel machines scheduling, while the second highlights the similarities with the strip packing problem. Two heuristics based on a dichotomic approach are then introduced. An experimental study comparing their results to optimal solutions, lower bounds and an existing heuristic from the literature shows that the second proposed method performs the best and provides near-optimal or good results for realistic-size instances in reasonable computational times.
Keywords: scheduling; operating room; parallel machines; resource; strip-packing.
Spiral With Line Segment Directory for a Helix Search Path to Find a Randomly Located Target in the Space
by Mohamed El-hadidy
Abstract: In this paper we present more interesting search plan in the space to find a lost hanging black box in the water from a view point of computational stochastic geometry. The space is divided into cubic cells with knowing side length. There exist one searcher moves on Helix path along spiral with line segment directory. Depending on the target has known trivariate known distribution, we focus on the optimal geometry features such as curvature and torsion of the Helix search path which minimize the expected value of detecting the target. Assuming trivariate standard Normal distribution we present numerical example to show the applicability of this model.
Keywords: Computational Stochastic geometry; Search theory; Trivariate standard Normal distribution.
A Comprehensive Review of approaches used for solving Tele-center location allocation problem in geographical plane
by Rajan Gupta, Sunil K. Muttoo, Saibal K. Pal
Abstract: Setup of Tele-center is the world-wide approach for the establishment of Information and communication Technology Infrastructure in rural areas for the overall development of a country. It is a key resource under E-Governance plan in any country, but a major problem with their location allocation is the sustainability. Tele-center establishment require a suitable location to increase the beneficial effect to service seekers. In this research, multi-faceted problems faced by Tele-centers are highlighted. This paper presents a comprehensive study on Tele-centers location allocation problem and all the recent development in multi-facility location problem research area through more than 150 research papers from high ranked peer-reviewed journals. The research survey examines all the important parameters for the facility location problem and an objective function is also formulated for the same. Based on the survey literature, it is found that the new allocation methods based on Meta-heuristic algorithms are emerging. This study would be a useful contribution in the field of location science, Tele-center location allocation and application of Meta-heuristic algorithms in E-Governance.
Keywords: tele-centers; location allocation; common service centers; rural kiosks; meta-heuristic algorithms; e-governance; geographical plane; ICT for Rural region.
Performance analysis of asynchronous priority based Internet router under self-similar traffic input queueing system with Markovian input and hyper-exponential services
by Malla Reddy Perati, Ravi Kumar Gudimalla
Abstract: In this paper, queueing behaviour of asynchronous and priority based Internet router with self-similar traffic input is analyzed. As Markov modulated Poisson process (MMPP) emulates self-similar Internet traffic, it can be used as input process of pertinent queueing system. For quality of service (QoS) guarantee in a Broadband integrated services digital network (B-ISDN), partial buffer sharing (PBS) mechanism is promising one. Since, network traffic is asynchronous and of variable packet lengths, wavelength division multiplexing (WDM) technology is to be employed, according to which, each output port is modelled as multi server queueing system. Moreover, in the modelling, service times (packet lengths) are assumed to follow more general distribution, namely, hyper-exponential (Hk) distribution with k stages in parallel of service. For the said reasons, Internet router here is modelled as MMPP/Hk/s/C queueing system employing PBS mechanism. The performance measures, namely, high priority and low priority packet loss probabilities, and mean lengths of non-critical and critical periods are computed, and presented graphically. This type of analysis is useful in dimensioning the priority based asynchronous router with self-similar traffic input.
Keywords: Internet router; self-similarity; priority packets; partial buffer sharing; multi server queue; hyper-exponential distribution; critical and non-critical periods; loss probability.
On modeling hard combinatorial optimization problems as linear programs: Refutations of the "unconditional impossibility" claims
by Moustapha Diaby, Mark Karwan, Lei Sun
Abstract: There has been a series of developments in the recent literature (by essentially a same "circle" of authors) with the absolute/unconditioned (implicit or explicit) claim that there exists no abstraction of an NP-Complete combinatorial optimization problem in which the defining combinatorial configurations (such as "tours" in the case of the traveling salesman problem (TSP) for example) can be modeled by a polynomial-sized system of linear constraints. The purpose of this paper is to provide general as well as specific refutations for these recent claims.
Keywords: Linear Programming; Combinatorial Optimization; Computational Complexity; Traveling Salesman Problem; TSP; "P vs. NP.".
An Economic Production Quantity Model with Imperfection in Process, Unit Transportation Cost and Backordering
by Mubashir Hayat
Abstract: Determining batch quantity for manufacturer in an imperfect production setup is key issue during the last decade. Several mathematical models have been developed for this purpose but these models are lacking of the consideration of unit transportation cost in the system wide cost. It is quite clear, that nowadays the transportation account for a huge portion of the overall cost of a product. Therefore, there is a need to involve transportation cost into the model for better decision making process. In this way, the paper incorporate unit transportation cost in an imperfect environment and allowing backorders as well as random defects. Three cases have been modelled by assuming the defective rate following uniform, triangular and beta distribution. Sensitivity analysis has been carried out to point out the important specification of all the three cases of the model.
Keywords: EPQ model; Unit transportation cost; Imperfection in process; Backordering.
Evaluation and ranking of the banks and Financial Institutes Using fuzzy AHP and TOPSIS techniques
by Mohammadreza Hasanzadeh, Changiz Valmohammadi
Abstract: The main purpose of this is to evaluate and rank Credit/Financial Institutes of Tehran Stock Market. The criteria were determined based on prior research and securities stock market literature and also based on the factors taken into account in stock selection. After screening the criteria, and through holding interview with experts were eight criteria were finally chosen. Using Fuzzy AHP, relative weights were calculated for each criterion and ultimately based on these weights, banks were ranked using TOPSIS. The results reveal that Bank Pasargad, Karafarin Bank, and Day Bank ranked first, second, and third respectively. One of the major concerns of those companies investing in the securities stock market is identifying the shares or collection of shares that achieve a substantial return in comparison with other companies. Since the group of banks and credit/financial institutes under investigation are among the most renowned and leading agents in Irans capital market, the obtained results could serve as a guidance for investors form one hand and the owners of the survey institutions and banks on the other hand to take necessary measures in accomplishing their objectives. This evaluation and ranking model has significant applied potentials in shares investment of a company among other similar companies in the context of Iran.
Keywords: Securities Stock; Ranking; Multiple Criteria Decision Making (MCDM); Fuzzy Analytic Hierarchical Process (FAHP); TOPSIS; Iran.
Asset management strategies for wind turbines: keeping or retrofitting existing wind turbines?
by Suna Cinar, Saeed Rubaiee, Mehmet Bayram Yildirim
Abstract: In this study, a parallel replacement problem with retrofitting (PRP-R) model is proposed to determine the trade-off between retrofitting and replacing an asset. The primary objective is to identify the replacement, maintenance, and retrofitting schedule that optimise purchasing new assets, operation and maintenance (O&M) cost, and retrofitting cost under budget and production constraints resulting in a mixed-integer linear programming formulation. This model is applied to a case study of energy industry involving wind turbines (WTs). Results show that due to a lower O&M cost, retrofitting is less costly than keeping the WTs. In addition, the effects of key parameters such as O&M cost, retrofitting cost, budget allocated for retrofitting, and governmental subsidy on the optimal replacement policy on total cost are studied. This research contributes a model that can be used to determine if WT retrofitting is economically justified and provides a rigorous analytical framework for optimising the decision-making process over the wind farm life cycle.
Keywords: wind turbine; mixed-integer linear programming; asset management; retrofitting; optimisation.
A Game Theoretic Approach for Integrated Pricing, Lot-Sizing and Advertising Decisions in a Dual-Channel Supply Chain
by Javad Zarei, Morteza Rasti-Barzoki, Seyed Reza Hejazi
Abstract: This paper discusses the coordination of pricing, lot-sizing and advertising policies in a dual-channel supply chain including one manufacturer and one retailer. The manufacturer produces one type of product and sells it to the retailer with wholesale price and, also, directly to consumers through direct channel. Consumers buy the product with retail price from the retailer and with direct sale price from the manufacturer. Demand depends on price and advertising efforts. Decision variables of the manufacturer are the wholesale price, the direct sale price, the amount of national advertising, and the participation rate of the local advertising. Decision variables of the retailer include the retail price, the inventory cycle time and the amount of local advertising. Relationship between the manufacturer and the retailer has been modeled by two non-cooperative games of Nash and Stackelberg-retailer and one cooperative game. Finally, the change effect of the important parameters on the profit functions and the decision variables has been investigated. The results show that with increasing the cross-price sensitivity, the manufacturers equilibrium profit for the two non-cooperative games increases. However, the increase in this parameter has no effect on the retailers equilibrium profit.
Keywords: Supply chain; Pricing; Lot-sizing; Advertising; Game theory.
Development of Maze Puzzle Algorithm for the Job Shop Scheduling
by Manhar Kagthara, Mangal Bhatt
Abstract: Maze Puzzle Concept has been introduced for solving job shop scheduling problem. Maze Puzzle Algorithm (MPA) is based on Rotation and Random Jumping which explores the solution space as well as exploits the solution near to optimum. Coding is done using MatLab software, Benchmark problem is evaluated for assessing efficiency of the algorithm. Results can be used for optimization of makespan for the given problem. The results are compared with other methods like GA, SA, SBI,SBII, PSO, BBO and TS, and found better than GA, SA, SB I, PSO, BBO but poor than SB-2 and TS.
Keywords: Maze Puzzle; Optimization; Job Shop Scheduling; Makespan; MATLAB; Jumping; Rotation.
Inventory model based upon order criticality
by Kamal Sanguri, Sri Vanamalla Venkataraman
Abstract: For a typical distribution network we can segregate the demand at the central demand catering centre as critical if the order is triggered by a direct customer requirement and non-critical if the replenishment order is triggered for routine stocking purpose by the customer touch point. In the traditional model discussed in literature the segregation is based upon the use based criterion, with the objective of keeping the cost of ordering, shortages and inventory at a minimum level. Through this research we propose a modification of the traditional model by incorporating different classes of demand based upon their criticality while maintaining appropriate service levels to them.
Keywords: inventory; rationing; service levels.
Flexible shift scheduling for a call centre using column generation
by Carlos Campos Amezcua, Omar G. Rojas, Emilio Zamudio Gutierrez, Elias Olivares-Benitez
Abstract: A shift scheduling problem for a call centre is solved by applying a column generation method. The objective is to find an employee schedule within the planning horizon that meets staffing requirements provided by the customer and that minimises the costs of excess-staffing and under-staffing. To solve the problem, two iterative models were used: the first being a mixed integer-linear programming model, the master problem, and the second being a column generation model. In a first approach, the master problem was solved considering a fixed number of shift patterns. The second approach was to use both models iteratively to generate shift patterns to be used in the master problem. Both approaches met all constraints relatively fast, but the column generation model decreased the cost considerably when cheaper shift patterns were generated. The models contribute to the current literature by providing flexibility to consider under-coverage and over-coverage at two levels.
Keywords: shift scheduling; call centre; staffing; mixed integer linear programming; master problem; column generation; shift pattern; flexibility.
Reliability Models of a Series-Parallel System with Replacement at Failure
by Ibrahim Yusuf, Nura Khalil
Abstract: The paper deals with the availability analysis of a series-parallel system consisting of four subsystems: A11, B, C and A12. Subsystem B contains units b11 and b12 arranged in active parallel, subsystem C contains units c11 and g12 arranged in active parallel. The system is exposed to two types of failure. Type I failure is a unit failure called partial failure where the failed unit is replaced with new and identical one while type II is a common cause failure to either subsystem B or C in which the failed subsystem is replaced with new one. Two probabilistic models are discussed. In model I (system without common cause failure), the system has type I failure while in model II (system with common cause failure) the system has both type I and II failure. In both models, the system has three modes: full capacity, reduced capacity and failure mode. Failure and replacement rate were assumed to be exponentially distributed. Through the transition diagram, system of first order linear differential equations were developed and solved to obtain the explicit expressions for steady-state availability. At last, some numerical examples have been taken to clarify the results.
Keywords: Availability; series-parallel; common cause failure.
The Second Purchase Decision under Selling Price-Sensitive Stochastic Demand and Purchasing Price Uncertainty
by Xiangling Hu, Jaideep Motwani
Abstract: It is quite common for a retailer to stock a specific quantity of a product more than once during a certain time period and then sell the product to the customers during the selling season. The retailer also has the option to make a second purchase if there is a potential profit increase on account of the purchase. However, due to the stochastic spot market purchasing price and the selling price dependent random demand, the retailer needs to determine whether a second purchase is necessary and if so what are the corresponding order time, quantity, and selling price in order to maximize the expected profit. In this paper, we develop a reality-adaptable solution algorithm to simplify the solution procedure. We also run simulations to analyze the inventory decisions and profits when a second purchase is possible.
Keywords: Supply chain management; purchasing; pricing; Price Uncertainties; Price-Sensitive Stochastic Demand.
Energy-awareness scheduling of unrelated parallel machine problems with multiple resource constraints
by Bing-Hai Zhou, Jiaying Gu
Abstract: This study proposes a framework to investigate the multiple dimensions of consumer value in the context of mobile marketing, to better understand the factors impacting on mobile consumer perceived value. We primarily conducted a series of interviews based on means-end chain theory and in line with the interviews of 179 WeChat official account subscribers. Then, three-step surveys are taken; after revision and testing, the proposed scale and the final six-dimensional model (MCPV scale) emerged. In addition, another study was conducted to validate the scale model, which takes mobile advertising effectiveness, attitudes towards the official account and loyalty towards the official account as consequences of MCPV. The MCPV scale was found to be both reliable and valid; it had significant influence on mobile advertising effectiveness, and it could serve as a framework for further empirical research in the mobile marketing settings. Lastly, theoretical and practical implications were discussed.
Keywords: multi-objective; energy consumption; resource constraints; artificial immune algorithm.
Bargaining in a closed-loop supply chain with consumer returns
by Yertai Tanai, Emmanuel Dechenaux, Eddy B. Patuwo, Alfred Guiffrida
Abstract: The increasing quality of consumer returns creates substantial economic potential for closed-loop supply chains to extract value from returned goods. In this paper, we focus on the supply chain interaction between a retailer and a third party reverse logistics provider (3PRLP) to process consumer returns under a full refund policy. In the model, the retailer orders processed returns from the 3PRLP in exchange for a fee and then resell the processed returns. We compare an uncoordinated supply chain, in which the retailer makes a take-it-or-leave it offer of a fee to the 3PRLP, to a coordinated supply chain, in which the retailer and the 3PRLP jointly decide on the quantity of processed returns and the fee using Nash bargaining. We show that coordination leads to both a higher quantity processed and a higher fee than in the uncoordinated case. We also derive a set of sensitivity results with respect to important parameters.
Keywords: Closed-loop supply chain; third party reverse logistics providers; supply chain coordination; consumer returns; Nash bargaining.
Analysis of a Variant Working Vacation Queue with Customer Impatience and Server Breakdowns
by Vijaya Laxmi Pikkala
Abstract: This paper analyses an infinite buffer single server variant working vacation queueing model in which the arriving customer may balk and the server is subject to breakdown. The service times during regular busy period and working vacation period, vacation times, breakdown times and repair times are assumed to be exponentially distributed and are mutually independent. In working vacation period, the customer may renege due to impatience. We derive the probability generating function of the steady-state probabilities and obtain the closed form expressions of the system size. In addition, we obtain some performance measures and a total expected profit function per unit time is designed to determine the optimal values at the maximum profit. We employ the particle swarm optimisation method to solve the profit optimisation problem.
Keywords: Queue; Balking; Reneging; Variant Working Vacations; Server breakdowns; PSO.
Efficiency Measurement of Canadian Oil and Gas Companies
by Mohamed Dia, Pawoumodom Matthias Takouda, Amirmohsen Golmohammadi
Abstract: In this study, we perform an efficiency analysis of Canadian oil and gas firms. Using data envelopment analysis, technical, managerial and scale scores from ten samples built from 110 oil and gas companies, listed in Canadian stock exchanges, are computed for the years 2012, 2013, 2014, and 2015. Our analysis, supported by appropriate statistical tests, confirms that the Canadian oil and gas industry exhibited predominantly low overall technical efficiency levels both for each of the years and overall for the four years. We have observed that the main source of inefficiencies was the management of operations. In addition, we have seen consistently that across the samples, a statistically significant relationship exists between the efficiency scores and the size of the companies. Finally, we have observed the existence of a relationship between the efficiency scores and the type of producer (pure oil vs. oil and gas), but we could not reach conclusions on the best performer that was consistent across the samples.
Keywords: Data Envelopment Analysis; Efficiency; Oil and Gas companies; Canada.
A dynamic programming approach for solving the economic lot scheduling problem with batch shipments
by Christoph Glock, Fabian Beck
Abstract: This note investigates the economic lot scheduling problem (ELSP) with batch shipments. It first modifies an existing formulation of the ELSP to account both for the cases of equal-sized and geometrically increasing batch shipments, and it then adapts the popular dynamic programming approach of Bomberger to the new planning situation. In addition, the paper specifies some steps of Bomberger's solution procedure that had been formulated imprecisely in the original publication of the author. The paper compares the solution approach proposed in this note to the popular methods of Hanssmann as well as Haessler and Hogue in a numerical experiment and highlights the influence of the batch shipments on the relative performance of the solution procedures. Our results show that the proposed modification reduces the performance disadvantage of Bomberger's basic period approach, which may be interesting especially for practitioners that are interested in an easy-to-apply procedure for solving the ELSP in practice. Our changes to Bomberger's solution procedure support finding the lowest total cost solution that had not always been obtained in earlier publications.
Keywords: Economic Lot Scheduling Problem; Bombergers method; Basic-Period-Approach; batch shipments; dynamic programming.
NEW MODIFIED RATIO TYPE ESTIMATOR OF POPULATION MEAN USING KNOWN MEDIAN OF STUDY VARIABLE
by Dinesh K. Sharma, S.K. Yadav, S.S. Mishra
Abstract: In this paper, we propose an estimator that utilises the known value of the population median of the study variable under simple random sampling without replacement (SRSWOR). As per requirement and for comparison, we derived the bias and mean squared error (MSE) of the proposed estimator to the first degree of approximation. The optimum values of the characterising constants involved in the proposed estimator were also obtained. The characterising scalar may take different values which results in different values of MSE of proposed estimator; therefore its optimum value was obtained. The least value of MSE of proposed estimator is derived for this optimum value of characterising constant. Efficiency comparison of the proposed estimator has been made with other existing competing estimators of the population mean which makes use of auxiliary information under SRSWOR. Theoretical findings regarding the proposed estimator have been verified through the numerical study. It has been shown through a numerical example that the proposed estimator has the least MSE among other existing competing estimators of the population mean of the study variable.
Keywords: Population median; Auxiliary variable; Bias; Mean Squared Error; Relative efficiency.
A Linear Algorithm for a Minimax Location of a Path-Shaped Facility with a Specific Length in a Weighted Tree Network
by Fatma Elsafty, Abdallah Aboutahoun
Abstract: This paper considers the problem of locating a path-shaped facility of a specific size on a weighted tree network. The criterion for optimality used in this paper is the minimax criterion in which the weighted distance to the farthest vertex from the facility is minimised. The minimax criterion gives acceptable results from the point of view of the fast response for the clients who are located far away from the facility. This location problem usually has applications in computer science, information science, and operations research. An O(n) time algorithm is proposed for finding the optimal location of a path-shaped facility of a bounded length on a weighted tree network, where n is the number of vertices in the tree.
Keywords: Tree network; facility location; minimax criterion; central path.
Developing a new chance constrained modified ERM model to measure performance of repair and maintenance groups of IRALCO
by Mohammad Izadikhah
Abstract: Evaluating the performance of repair and maintenance groups is recognised as a key component of improving the strategic and operational levels. The purpose of this paper is to develop a model to evaluate the maintenance groups of IRALCO. Data envelopment analysis (DEA) is a useful method for measuring performance of repair and maintenance groups. However, in real problem there might be stochastic data. For this purpose, this paper presents a new stochastic modified enhanced Russell measure (ERM) model to measure performance of repair and maintenance groups. An important property of the proposed model is stated and proved as a proposition. A case study demonstrates applicability of our approach.
Keywords: Data envelopment analysis (DEA); Chance-constrained data envelopment analysis; Stochastic DEA; Efficiency; Stochastic data.
Worm Optimization Algorithm to minimize the Makespan for the Two-Machine Scheduling Problem with a Single Server
by JEAN-PAUL ARNAOUT
Abstract: This paper considers the problem of scheduling a given set of jobs on two identical parallel machines. Each job must be processed on one of the machines, and prior to processing, the job is set up on its machine using one server; the latter is shared between the two machines. This problem is known as the two-machine scheduling problem with a single server, and our objective is to minimise the makespan. A worm optimisation algorithm (WO) is introduced for this NP-hard problem, and its performance is compared to ant colony optimisation, simulated annealing, and genetic algorithm, as well as an exact approach. The superiority of WO over the other algorithms is obtained through extensive computational results.
Keywords: Worm Optimization; Parallel Machines; Single Server.
Usage of Pedestrian Bridge among the Urban Commuters in Kuala Lumpur: A Conceptual Analysis and Future Direction
by Siti Norida Wahab, Lay Yan Feng, Koay Wui Lim, Amir Aatieff Amir Hussin
Abstract: Pedestrian bridges are one of the safest crossing facilities, allowing pedestrians to cross the road by diverting away from traffic. However, the usage of pedestrian bridges has not been popular in Malaysia. Dreadful conditions of pedestrian bridges further contribute to the low usage. The main aim of this research is to develop a conceptual model to identify the motivating factors affecting the usage of pedestrian bridges among urban commuters in Kuala Lumpur. Four factors were identified from an extensive review of literature to construct the model namely safety, attitude, facilities and convenience. This study serves valuable information for scholar to further study and analyse topic relating to the use of pedestrian bridges. From the practitioners point of view, this study provides valuable information to policy makers and government authorities to integrate and focus on the most motivating factors that could attract more users into utilising pedestrian bridges.
Keywords: Pedestrian Bridge; Urban Travelling Environment; Urban Commuters; Mobilities.
A new Particle Swarm Optimization variant based experimental verification of an industrial robot trajectory planning
by Mahalakshmi S, A. Arokiasamy
Abstract: A new variant of particle swarm optimisation (PSO), constriction coefficient neighbourhood varying inertia weight varying acceleration coefficients particle swarm optimisation (CNVIWVAC PSO) and a conventional differential evolution (DE) algorithms are proposed in this work to do optimal time mechanical energy trajectory planning for an industrial robotic manipulator (MTAB ARISTO 6XT). Three pick and place operations are considered. Minimisation of travelling time of robot end effector and mechanical energy of the actuators are considered as objective functions. This is to ensure fast execution of the desired operation in a minimum possible spending of mechanical energy. All kinematic and dynamic constraints such as position, velocity, acceleration, jerk and torque bounds are considered to ensure smooth as well as practical trajectory. Two stationary obstacles are considered in the path robot manipulator. A comparative analysis of proposed algorithms (DE and CNVIWVAC PSO) with a point-to-point (PTP) algorithm (own system of the MTAB ARISTO 6XT robot) has been carried out by means of experimental tests. The proposed algorithms have been evaluated and experimentally validated. The results proved that the proposed algorithms are better than the existing system (PTP) of robot.
Keywords: Industrial Robot; MTAB ARISTO 6XT robot; minimum time-energy Trajectory planning; pick and place operation; DE; CNVIWVAC PSO.
AN ANALYSIS AND MODELING OF SELECTED BARRIERS FOR SUSTAINABLE MANUFACTURING SYSTEM USING ISM TECHNIQUE
by Subrata Kumar Patra, Tilak Raj, B.B. Arora
Abstract: Traditional manufacturing is largely focused on economics but are attributable for environmental degradation, ecological imbalance and several other negative connotations. To cope up with these challenges, manufacturers have to adopt manufacturing practices amenable to sustainable manufacturing system (SMS). However, transitioning to SMS is a challenging task because a gamut of complex barriers hinders its successful implementation. Data extracted from the reviewed literatures and aptly complimented by the responses of experts, helped in identifying various barriers that are considered as critical from sustainability viewpoint. A focused group discussion (FGD) group consisting of a few experts from steel industry prepared a questionnaire incorporating these barriers. An analysis using interpretive structural modelling (ISM) reveal that lack of government support towards developing new technologies is the most important barrier towards the attainment of SMS. Insights from this analysis will benefit decision makers in formulating suitable strategies to mitigate the ill-effects of these barriers.
Keywords: traditional manufacturing; barriers; SD; sustainable development; sustainability; SMS; sustainable manufacturing system; ISM; interpretive structural modeling; transitivity.
Modelling the sustainable supply chain management practices in Indian industries: A business model using Fuzzy TOPSIS approach
by K. Mathiyazhagan, Sarthak Ahuja
Abstract: Worldwide customers have awareness for the use of eco-friendly products and rules and regulations imposed by government to move towards the sustainable products creates a pressure impact on the industries. The objectives of this study are to develop model and prioritise major sustainable supply chain management (SSCM) practices in Indian industries with a specific focus towards automobile, textile and food sectors. Considered the 19 SSCM practices from the literature review and discussion with experts. The fuzzy technique of order preference for similarity to ideal solution (TOPSIS) has been used to rank. Results shows that practices ISO 14000 and 14001 certification, value stream mapping and corporate social responsibility have been ranked as the topmost priority while taking decisions to ensure perfect sustainability in supply chains.
Keywords: Sustainable Supply Chain Management (SSCM); Technique of Order Preference for Similarity to Ideal Solution (TOPSIS).
A Review and Classification of Heuristic Algorithms for the Inventory Routing Problem
by Stella Sofianopoulou, Ioannis Mitsopoulos
Abstract: The inventory routing problem (IRP) is an integration of vehicle routing and inventory management problems. In the recent years, it has increasingly drawn the attention of the researchers because of its potentially significant practical value. The IRP is classified as NP-hard problem since it subsumes the vehicle routing problem (VRP). This fact led to the development of many heuristic or metaheuristic approaches, although a small number of exact methods have been introduced recently. Heuristic methods offer the advantage of shorter time scales, i.e., greater computational efficiency, on the expense of course of the accuracy of the results. The immediate trigger for this study is our concern about results validation, which has been debatable in early papers, and only recently a systematic effort to create a set of optimally solved benchmark instances has been made. This article presents the heuristic methods for solving the basic variants of IRP found in the literature, stressing the computational results and the solution verification approach, rather than the methodology of the algorithms. The paper concludes with a discussion on the quality of the performance assessment of the proposed algorithms.
Keywords: Inventory routing problem; heuristic algorithms; literature review; results validation.
An M^[X]/G(a,b)/1 queueing system with unreliable server, stand-by server, restricted arrivals, variant threshold policy for vacations
by Karpagam Viji, Ayyappan G
Abstract: We studied the behavior of an M^[X]/G(a, b)/1 queueing system with unreliable server, stand-by server, restricted admissibility and variant threshold policy for vacations in this paper. The stand-by server is utilised only during main servers repair period. At the moment of main servers busy completion or repair completion, if the number of customers in the queue is less than
Keywords: General bulk service; Breakdown and repair; Stand-by server; Restricted admissibility; Variant threshold policy for vacations.
Fuzzy Expectation-spread-skewness model for Shariah-Compliant Portfolio Optimization
by Imen Ben Abdelwahed, Faouzi Trabelsi
Abstract: It is well known that fuzzy portfolios are very useful for investors who are looking for a path to manage risk when dealing with their long-term investment portfolio. In this paper, we propose a new framework to portfolio selection problem based on fuzzy theory in the context of Islamic finance. In order to measure how much an investor satisfies with his profit, skewness is adopted in addition to the first two moments of the distribution. We formulate a new fuzzy (Shariah-compliant) portfolio optimisation problem, referred as fuzzy expectation-spread-skewness (FESpS) model. We discuss the existence of the optimal solution. Besides, we provide numerical methods to approximate the solution, following in parallel probabilistic and analytical approaches. Some examples of application are also studied. Finally, we compare the followed numerical approaches and we state some financial interpretations.
Keywords: Fuzzy portfolio; skewness; Shariah-compliant portfolio; Fuzzy expectation-spread-skewness (FE ? Sp ? S) model; triangular fuzzy variable; spread; analytical approach; probabilistic approach.
Extreme learning machine based investigation on automated detection of architectural distortion in mammograms
by Malar E, Deepan Chakravarthi P
Abstract: Breast cancer, having its origin from the breast tissue is usually detected by mammographic screening. The early detection of breast cancer reduces the mortality rate. A subtle type of breast cancer that often leads to misinterpretation by radiologists is architectural distortion. Though the existing computer aided diagnosis systems efficiently and effectively detect the presence of micro-calcification and masses, the diagnosis of architectural distortion lacks a promising method. This project attempts to detect and classify the regions of mammograms having architectural distortion. MIAS and DDSM database images are enrolled in this research work. 350 region of interests (ROIs) of each architectural distortion and normal cases were extracted. They were subjected to a filtering process, followed by contrast enhancement. Application of Gabor filter to the images resulted in orientation differences between the normal and abnormal images. Statistical features extracted from the resulting images were classified using extreme learning machine classifier. The experimental results obtained from extreme learning machine in comparison with support vector machine had an accuracy of 98.49% and 87.21% for MIAS and DDSM respectively. The accuracy of combined database of which is 85.38%.
Keywords: Breast cancer; Architectural distortion; Extreme Learning Machine; Support Vector Machine; Gabor filters.
Fuzzy bi-objective model for hazardous materials routing and scheduling under demand and service time uncertainty
by Kamran Moghaddam, Jalil Kianfar
Abstract: We develop a fuzzy-based bi-objective optimisation model for hazardous materials (hazmat) vehicle routing and scheduling problem with time windows. The task is to find optimal links and routes to maintain a balance between safe and fast distribution of hazmats between origins and destinations through the transport network. We consider unknown probabilities for hazmat incidents along with a game-theoretic demon approach in a link-based model. Using the Nash game theory approach, an integrated routing and scheduling hazmat shipment problem is formulated. Since the formulated problem is a bi-objective model with travel time and population risk objectives, we also propose a solution method based on a hybrid Monte-Carlo simulation and fuzzy goal programming to obtain the set of Pareto optimal solutions. Computational results of a carefully crafted numerical example are also provided to illustrate the effectiveness of the developed mathematical model and the solution method in obtaining Pareto-optimal solutions.
Keywords: Hazardous material; Vehicle routing and scheduling problem with time windows; Fuzzy multi-objective optimization; Fuzzy goal programming.
A Novel Method of Variable Selection in Data Envelopment Analysis with Entropy Measures
by Zhaotong Lian, Qiang Deng, Qi Fu
Abstract: In data envelopment analysis (DEA) modelling applications, analysts typically experience difficulty in choosing variables when the number of variables is greater than the number of decision-making units (DMUs). In this paper, we develop a novel method to facilitate variable selection in DEA using entropy theory to avoid information redundancy. A numerical analysis is provided to compare our method to those of related studies. The results show that our proposed method produces a lower Akaike information criteria (AIC) value than other approaches. By presenting a real-world case, we show that this new method yields useful managerial results.
Keywords: Data envelopment analysis; Variables Selection; Entropy theory; Akaike information criteria (AIC).
Research and Development Project Funding and the Efficiency of Participating Companies - The Case of the Austrian General Program
by Drinko Kurevija
Abstract: This article analyses the performances of the Austrian Research Promotion Agency's (ARPA) general program. There was no significantly positive shift in best practice frontier for projects between 2009 and 2011. There was a significantly positive shift in the improvement of technology and a significantly negative shift in efficiency for the same period. Fama-MacBeth results show, with sales as the independent variable, that employees and R&D expense are significant but not project income. The stochastic frontier analysis reveals that the null hypothesis of no inefficiency effects is rejected. As anticipated and substantiated, by applying the more appropriate parametric approach, the results did not confirm the findings of Naveh (2005), showing a positive association with initial product development and efficiency. As part of their first phase of product development, the findings suggest the presence of inefficiency effects of firms that participated in ARPA's general program.
Keywords: data envelopment analysis; Malmquist index; Fama-MacBeth; stochastic frontier analysis; programs; projects; efficiency.
On the scheduling of handling equipment in automated container terminals
by Iñigo L. Ansorena
Abstract: This paper deals with the application of the flow shop scheduling problem (FSSP) to automated container terminals. After the description of operations between the quay line and the storage yard we analyse the flow of containers through six well established heuristic methods. Additionally, we apply a full enumeration mechanism to solve the FSSP. This technique enumerates all permutation schedules and picks the best one based on the specified criterion. A numerical illustration is given to clarify the application of FSSP techniques to container terminals. The paper suggests that the selection of the best heuristic is crucial to increase productivity and achieve goals, since it allows time savings around 20%-30% depending on the method used.
Keywords: Flow Shop; Sequencing Problem; container terminals; heuristic method; automation; guided vehicles; stacking cranes; quay cranes; operation time; time savings.
Project Management Best Practices and Project Success in Developing Economies
by Saleh Fahed Alkhatib, Sarah Khrais
Abstract: This study aims to identify project management best practices, their impact relationships and their role in construction projects success in developing economies. Based on project experts semi-structured interviews, several project management best practices have been identified and validated. First, the DEMATEL technique is used to analyse the impact relationships between these practices and to classify them into
Keywords: Project Management Best Practices; Construction Projects; Project Success indicators; Developing Economies Projects; DEMATE Technique; Jordan.
A Hybrid State Feedback Controller Design for Two Different Dynamic Systems
by ARAVIND PITCHAI VENKATARAMAN, Veeramani V, S.M. Giri Rajkumar
Abstract: This paper deals the application of error recursive reduction computational (ERRC) technique to improve the performance of state feedback (SF) with integral control design. To highlight the performance of the proposed method is tested on second order and third order system. A second order system is framed based on general assumptions and the third order system is a model of the aircraft pitch control system. The proportional and integral control gain values are obtained using Ackermann's method. Results of with and without ERRC in state feedback design are compared. The controller performance was verified in terms of rise time, settling time, overshoot and tolerance limits.
Keywords: ERRC method; state feedback; state space and higher order systems.
A DEA model towards efficiency estimation of biomass energy production of agro-energy districts
by ANNA KALIOROPOULOU, Thomas Bournaris, Vasileios Manos
Abstract: In this work, the data envelopment analysis (DEA) is applied for the estimation of relative efficiency in biomass energy production and for optimal organisation of farm planning in accordance to EU goals for renewable energy sources. Specifically, a DEA model with five inputs and one output was employed at the seven prefectures of Northern Greece. The inputs used for the purpose of this study are the main factors of agricultural production, i.e., the land available, the variable costs, the available tractors, the fertilisers and the labour used. The output is the electric energy from the biomass of crop residues and it is consistent with EU objectives for renewable energy sources. The application of the DEA model revealed four prefectures as relatively inefficient and three as relatively efficient. From the empirical analysis, a reorganisation and a better allocation of inputs for the inefficient prefectures is suggested.
Keywords: Relative efficiency; Data Envelopment Analysis; Biomass energy.
Duality of control problems in general Banach spaces
by S. Padhan, Chandal Nahak, P. Behera
Abstract: Control problems have been given a special attention to the theory of optimisation, which is concerned with problems involving infinite dimensional cases. Control problems along with various types of their duals are described in general Banach spaces. Under convexity assumptions on functionals, several duality (weak, strong and converse) results are established between control primal and the corresponding Mangasarian type dual problem. Again, the Mond-Weir type duality model is constructed to weaken the convexity condition to pseudo-convexity and quasi-convexity.ny nontrivial examples are given to support the efficacy of the new findings. It is found that some of earlier results are the special cases of the present investigations.
Keywords: Control problems; Convexity; Mangasarian type duality; Mond-Weir type duality.
Optimizing Preventive Maintenance Schedule for a Distillery Plant
by Ankur Bahl, Anish Sachdeava, R.K. Garg
Abstract: In today's era of automation, the maintenance of the complex systems is necessary for obtaining high payback ratios. The time has changed the thinking of plant/maintenance managers from fix-it-broken approach to preventive maintenance approach for bringing back the deteriorated components/systems to the predetermined operational conditions. But since the resources are limited therefore it is required to achieve an effective maintenance approach to minimise the total maintenance cost and equipment downtime. This paper presents the framework for deciding the optimal schedules for preventive maintenance under constraints of the maintenance cost, availability and revenue generation. The programming package Mathematica is used to solve the complex equations of the framework and finding optimal preventive maintenance schedule. The practical case of a distillery plant is considered is gauge the effectiveness of this approach.
Keywords: preventive maintenance; maintenance cost; availability; petri nets; optimum schedule.
Analysis of MAP/PH/1 retrial queue with constant retrial rate, working vacations, abandonment, flush out, search of customers, breakdown and repair
by G. Ayyappan, RANGANATHAN Gowthami
Abstract: A retrial queueing model in which the inter arrival times follow Markovian Arrival Process (MAP), the service times follow phase type distribution and the remaining random variables follow exponential distribution is studied in this paper. We use the matrix analytic method to study the resulting GI/M/1-type queuing model in the steady state. Some performance measures are enumerated. The analysis of the model has been done numerically and graphically.
Keywords: Markovian arrival process; Phase type distribution; Retrial queues; Orbital search; Working vacation; Breakdown and Repair.
Enhancing Reliability-Availability In Asset Management With Retrofitting-A Wind Turbine Case Study
by SUNA CINAR, Mehmet Yildirim, Ferenc Szidarovszky
Abstract: In this study, a mixed-integer linear programming (MILP) modelling approach is proposed to identify the optimum maintenance or retrofitting schedule under budget and energy production constraint(s) by improving failure rate of assets. The proposed reliability/availability asset management with retrofitting (RAAMWR) model seeks to maximise the total net profit subject to achieving a target reliability/availability value and minimise the total improvement cost subject to a budgetary constraint. We apply our model to a case study involving wind turbines (WTs). The results of this study show that to reach the target reliability value with improved failure rate data, model selects retrofitting due to lower loss time and high energy production rate of retrofitting options. This optimal retrofitting choice is not only due to low loss time, but also improving the existing failure rate of an asset to reach the target reliability. In addition, the effects of key parameters on total cost, such as operation and maintenance (O&M) cost, retrofitting cost, budget allocated for retrofitting, and different target reliability values on the optimal improvement policy were considered.
Keywords: mixed-integer linear programming; wind turbine; asset management; availability; retrofitting; optimization.
Exact and heuristic methods to solve the two-machine cross-docking flow shop scheduling problem
by Imen Hamdi, Yosr Hazgui
Abstract: In this paper we study the two-machine cross-docking flowshop scheduling problem. Cross-docking is an innovative logistical strategy in which truck is unloaded from inbound (supplier) vehicles and directly loaded into outbound (customer) vehicules without storage in between or less than 24 hours. We aim to determine the schedules of the inbound and outbound trucks in the crossdock while minimising the makespan. This problem is known to be NP-hard in the strong sense. We propose a mixed integer linear programming (MILP) which is tightened by adding valid inequalities. Also, we develop some heuristic methods which are based on some known and new priority rules. Then, we report the results of computational experiments on randomly generated problems.
Keywords: Cross-docking; Scheduling; MILP; Heuristics.
Computing Pareto set in the criterion space for bicriteria linear programs using a single criterion software
by François Dubeau, Marie Emmanuel Ntigura Habingabwa
Abstract: In case of a mathematical programming problem with conflicting criteria, the Pareto set is a useful tool for a decision maker. Based on the geometric properties of the Pareto set for a bicriteria linear program, we present a simple method to compute this set in the criterion space. We describe completely the algorithm and analyse its complexity. We illustrate the method by solving in details two simple examples. It is important to observe that the method requires only a basic linear program solver.
Keywords: Bicriteria linear program; efficiency set; Pareto set; criterion space; weighted-sum.
Multi objective inventory model for material resource planning with uncertain lead-time
by Heibatolah Sadeghi, Anwar Mahmoodi
Abstract: MRP, in its original form, utilises deterministic lead-time. However, the lead-time uncertainty is a fact of life in most of production systems. Therefore, developing MRP to deal with lead-time uncertainty is of great importance to academics and practitioners. In this paper, the problem of supply planning is considered in a multi-period, multi-level assembly system in which each sub-level has several components whose lead-times are uncertain. A two-objective mathematical model is presented not only to provide the appropriate number of periods in POQ policy, but also to determine the planning lead-time of each sub-level component. The aim of the model is to minimise the expected total cost, and to maximise the customer service level. Furthermore, two metaheuristic algorithms, namely non-dominated sorting genetic algorithm-II (NSGA-II), and multi-objective particle swarm optimisation (MOPSO) are proposed to solve the model. Finally, numerical experiments are carried out to compare the effectiveness of the procedures.
Keywords: Supply planning; random lead-time; Customer service level; periodic order quantity; Multi-objective genetic algorithm.
Detailed scheduling distribution of real multi-product pipeline
by Asma Berrichi
Abstract: We conducted an optimisation study on an Algerian multi-product pipeline that supplies market with fuels, through two fuel centres. The successive transport results under the diffusion effect, an interface between two products in contact, this interface is no marketable in any case and must be stored separately to the pure products. The number of interfaces depends mainly on the number of transported batches. Once the interface is at the end of the pipeline, and when the storage tanks reach the high level, the pumping is interrupted, a situation that can cause fuels shortage of on the market. The mixed integer linear programming 'MILP' formulation was able to respond to our problem and eliminate the high-level of mixture tanks constraint, by scheduling the multi-product pipeline, considering real operating conditions: Injection flow rate of each product, the products transport sequence, the imperative storage of the interface at the 2nd fuel centre, etc.
Keywords: multi-product pipeline; petroleum products; interfaces; MILP formulation.
A complete ranking of decision making units with interval data.
by Somayeh Khezri, Gholam Reza Jahanshahloo, Akram Dehnokhalaji, Farhad Hosseinzadeh Lotfi
Abstract: Interval data envelopment analysis (interval DEA) deals with the problem of efficiency assessment when the inputs and/or outputs of decision making units (DMUs) are given as interval data. This paper focuses on the problem of ranking DMUs with interval data. First, we define extreme efficient units, super efficiency score, the best and the worst efficiency (inefficiency) frontiers in the interval DEA context. Then, we propose a novel method based on the lower and upper super efficiency scores of a unit under evaluation and the distance of that unit to four developed frontiers. Our method ranks all efficient and inefficient units which is one of the main advantages of it. Our method uses several essential criteria simultaneously to rank units with interval data. These criteria increase the discrimination power of our proposed method. Potential application of this method is illustrated with a dataset consisting of 30 branches of the social security insurance organisation in Tehran.
Keywords: Data Envelopment Analysis; Interval DEA; Decision Making Units; Ranking.
Bounding Strategies for obtaining a lower bound for N-job and M-machine flowshop scheduling problem with objective of minimizing the total flowtime of jobs
by Shankar Saravana Kumar, Rajendran Chandrasekaran, Rainer Leisten
Abstract: In this paper, bounding strategies for determining a lower bound on the completion time of a job sequenced in each position in the permutation sequence on each machine in permutation flowshop scheduling problem with minimisation of total flowtime of jobs as objective are discussed. Basically, the bounding strategies are machine-based bounding strategies used for determining the lower bound on total flowtime of jobs for all the small-sized and large-sized benchmark flowshop scheduling problem instances proposed by Vallada et al. (2015). The lower bound matrix can be pruned as tightening constraints into the mixed integer linear programming (MILP) model with objective of minimisation of total flowtime of jobs. Since the flowshop scheduling problem with total flowtime objective is difficult, two kinds of linear programming (LP) relaxation methods are used for determining an LP-based lower bound on total flowtime of jobs for some benchmark problem instances proposed by Vallada et al. (2015).
Keywords: scheduling; flowshop; total flowtime of jobs; lower bound.
The Effect of Order Incentives in a Multi-product Dynamic Inventory Model
by Bin Zhou, Hua Zhong, John Wang
Abstract: This paper addresses the challenges in a multi-product inventory system with uncertain demands. As an incentive, free shipping is used by the supplier to stimulate large orders from customer, who reviews and restocks inventory periodically. The model is first established through stochastic dynamic programming and the characteristics of the optimal policies are analysed. As the optimal control policies are found very complex, accurate lower bound approximations and effective heuristic policies are proposed and discussed. Finally, performance of the lower bounds and heuristics is evaluated through extensive numerical studies which also evaluate the effects of key system parameters including service levels and varied costs.
Keywords: Production and inventory management; Multiple products; Stochastic model; Heuristics.
Optimal Multiplicative Generalized Coordinated Search Technique to Find a D-Dimensional Random Walker
by Mohamed El-hadidy, Ajab Alfreedi, Alaa Alzulaibani
Abstract: This paper presents a new interesting search model that minimises the expected value of the total detection cost of the d-dimensional random walk target with maximum probability. This technique is called a generalised coordinated linear search technique with multiple searchers. The target may be in one of d-direction (dimension) inside the space. We study this technique from a probabilistic and optimisation point of view where each direction is considered as a cylinder and it is searched by two searchers. They start the searching process from any point rather than the origin. The targets initial position is a random variable vector have a known probability distribution. We show the existence of a finite search plan by using the analytical methods. To minimise the expected value of the first meeting time between one of the searchers and the target, we should discuss the existence of the optimality conditions for this search plan and then find the optimal search plan.
A numerical example illustrates the effectiveness of this technique.
Keywords: coordinated search technique; optimality conditions; d-dimensional random walk target.
Strategic Inventories in a Supply Chain with Downstream Cournot Duopoly
by Xiaowei Hu, Jaejin Jang, Nabeel Hamoud, Amirsaman Bajgiran
Abstract: The inventories carried in a supply chain as a strategic tool to influence the competing firms are considered to be strategic inventories (SI). We present a two-period game-theoretic supply chain model, in which a singular manufacturer supplies products to a pair of identical Cournot duopolistic retailers. We show that the SI carried by the retailers under dynamic contract is Pareto-dominating for the manufacturer, retailers, consumers, the channel, and the society as well. We also find that retailer's SI, however, can be eliminated when the manufacturer commits wholesale contract or inventory holding cost is too high. In comparing the cases with and without downstream competition, we also show that the downstream Cournot duopoly undermines the profits for the retailers, but benefits all others.
Keywords: Supply chain coordination; Game-theoretic modeling; Strategic inventories; Contracts; Cournot duopoly.
A chance constrained closed-loop supply chain network design considering inventory-location problem
by Mehdi Biuki, Hassan Mina, Parisa Mostafazadeh, Shiva Zandkarimkhani
Abstract: The design of reverse supply chain networks is one of the major solutions for the reduction of solid waste and use of resources for producing product to a lesser extent. The design of a reverse supply chain network leads to reduced costs in addition to reducing environmental detrimental effects. Therefore, this paper seeks to develop a mixed integer linear programming (MILP) model for designing a closed-loop supply chain network (CLSCN) under uncertainty. The under study network is multi-product, multi-period and multi-echelon wherein the possibility of storage and facing shortage in the back-order type has been considered. An approach based on chance constrained is applied for controlling uncertainty. In order to investigate the efficiency of the proposed model, we implemented it in an automotive manufacturing industry in Iran where the results of model implementation through real-world data in GAMS software, as well as the results of sensitivity analysis of demand values indicate the precise function and the accuracy of the results.
Keywords: Closed-loop supply chain; Inventory-location problem; Mathematical programming model; chance constrained theory.
Comparing Time-Stable Performance of Staffing Methods using Real Call-Center Data
by ARKA GHOSH, Dong Dai, Keguo Huang
Abstract: A central question in capacity management for service systems is to decide the number of servers that changes over time to accommodate time-varying arrivals and maintain a prescribed service-quality level. Two common methods for this are: square-root-staffing formula (SRSF) and iterative-staffing algorithm (ISA). We examine the stability of these two methods on simulated data from a probabilistic model and on a synthetic data created by resampling actual arrival, service and abandonment times from the call-centre of an Israeli bank. We use the delay probability as well as other common measures for the quality of service. In the simulated case, the ISA method marginally outperforms the SRSF method in maintaining the stability around the target delay probability. But in the case of synthetic resampled data, the stability drops when the service and patience rates are large. We also give theoretical proofs for the convergence of the ISA method under appropriate conditions.
Keywords: staffing; call-centers; capacity planning; re-sampling; data analysis; queues with time-varying arrivals.
Reliability optimization of parallel-series system with interval valued and fuzzy environment via GA
by Anushri Maji, Asoke Kumar Bhunia, Shyamal Kumar Mondal
Abstract: Reliability is an essential implement for a system. In this paper, we have considered a reliability optimisation problem in parallel-series system. Here, we have discussed about that how many components are needed to maximise the system reliability with some resource constraints such as cost, weight, volume, etc. Also, to get more relaxation we have assumed that the component reliabilities are interval valued number, lie between 0 and 1. Here, the constraint coefficients have been taken in fuzzy environment. Also, the fuzzy constraints have been defuzzified using possibility and necessity measures. The interval valued system reliability has been reduced to precise form applying centre-radius method. After reduction, our problem has been converted to a multi objective reliability optimisation problem with cost, volume, weight etc. as constraints. Finally, the proposed model has been illustrated numerically to study the feasibility of the system considering a real life example which has been solved by multi-objective genetic algorithm (MOGA).
Keywords: Parallel-series system; Interval valued component reliability; System reliability; Fuzzy constraint coefficients; Genetic algorithm.
A green closed-loop supply chain network: a bi-objective mixed integer linear programming model
by Hassan Mina, Farhad Salehian, Sobhgol Gholipour, Hassan Lamouchi
Abstract: The increasing level of customer awareness and application of environmental laws by governments, on the one hand, and the increasing number of competitors, on the other hand, has obliged industry owners to include green activities in the design of the supply chain network. Green activities include any type of action that reduces environmental degradation. Hence, this study seeks to develop a bi-objective, mixed integer linear programming (MILP) model for designing a green supply chain network. In the proposed model, the minimisation of costs and detrimental environmental effects are discussed. LP metric method is used to solve the bi-objective model and the proposed model is run by using simulated data in small and medium sizes in GAMS software. Finally, the model sensitivity analysis is measured in order to evaluate the validity and performance of the proposed model by using demand-driven scenarios. The results of this model indicate the effectiveness of the proposed model.
Keywords: Green supply chain management; Closed-loop supply chain network; Mathematical model.
Solving a Single Period Inventory Model with Fuzzy Inequality
by Anuradha Sahoo, J.K. Dash
Abstract: The purpose of this paper is to present a fuzzy chance-constrained single period inventory model (FCCSPIM) in which the fuzziness appears in the space constraint and objective function is crisp. Here the partial order relation exists in between a random variable and a real number. That means the probability of the event is discussed under vague data. Our approach for the solution process uses mostly fuzzy Zimmermann technique to convert the FCCSPIM into a proper deterministic equivalent. Then the resulting nonlinear deterministic model is solved by using LINGO software. The esult indicate that the fuzzy programming approach is effective for the inventory problem. The applications of an optimisation model under uncertainty are used to solve day to day problems. Many methods were developed by using tools of mathematics, probability theory and stochastic process. Here, one new approach of fuzzy programming technique is introduced to obtain a deterministic form.
Keywords: Single period inventory model; Chance constrained programming problem; Fuzzy partial order relation.
Analysis of unreliable bulk queueing system with overloading service, variant arrival rate, closedown under multiple vacation policy
by Nirmala Marimuthu, Ayyappan G
Abstract: In this article, an unreliable single server bulk queueing model with overloading service, variant arrival rate, closedown under multiple vacations are considered. The arrival rates of the units are different and depends upon the server status. On service completion epoch, if the queue length is less than 'a' then the server perform closedown work. Following the closedown, the server leaves for multiple vacations of random length. We incorporated the overloading concept for the server which is assured in various practical applications. When the server is in working modes, breakdown may occur randomly at any instant during essential/overloading service. The repair job of broken down machine is done immediately and the server returns to render its remaining service. We derived the probability distribution of queue length at a departure and random epoch using supplementary variable technique. Various performance indices namely the expected length of the queue; the expected waiting time in the queue are obtained. Stability condition and steady-state probabilities are also established. In order to match our investigation with the earlier existing results, we discuss some particular cases. Finally, numerical illustration along with graphical studies are presented to visualise the effect of the parameters of the system.
Keywords: General bulk service; Overloading service; Breakdown and repair; Closedown; Multiple vacation; Variant arrival rate.
Selecting the most agile manufacturing system with respect to agile attribute- technology- Fuzzy AHP approach
by Ritu Chandna
Abstract: Agility of manufacturing systems is defined as the competence of existing and prospering in surroundings which are affected by continuous and unpredictable change and competition. Technology plays a vital role in helping managers to achieve agile manufacturing. Technology is changing rapidly and the manufacturing systems have to change their processes, structures, products and services accordingly to survive profitability in the market. Technology has four qualities which are knowledge about technology, finding direction in modern technology, ability in making technology more effective and a resilient method of bringing out technology. This paper uses fuzzy AHP approach to select the most agile manufacturing system with respect to these technological dimensions. This research will help decision makers to initiate technological innovations in manufacturing processes, to improve and help to control and evaluate the quality of emerging technologies and expand them for adoption. The analysis shows that flexible production technology parameter is more important as compared to other parameters.
Keywords: Agility; manufacturing system; technology; fuzzy AHP; competition.
Estimating Peppermint Oil Yields with Auxiliary Variable Information
by Dinesh K. Sharma, S.K. Yadav, Kate Brown
Abstract: In this article, we propose an improved method for estimation of the population mean using an auxiliary variable and apply it to the peppermint oil yield for a block level in the Barabanki District of Uttar Pradesh State in India. We consider a new family of estimators for the population mean, using the area of the peppermint field as the auxiliary variable. We study the sampling properties of the proposed family, through the bias and the mean squared error (MSE) to the first order approximation. We compare the suggested estimators with competing estimators theoretically and verify the conditions under which they outperform the competing estimators with actual data collected from the Siddhaur Block of the Barabanki District.
Keywords: Study Variable; Auxiliary variable; Regression-cum-Ratio estimator; Bias; MSE; PRE.
Development of IFDEA models for IF Input-oriented Mix Efficiency: Case of Hospitals in India
by Alka Arya, Shiv Prasad Yadav
Abstract: In conventional input-oriented mix efficiency (IOME), the input-output data are crisp numbers. But these data fluctuate in real world applications. Intuitionistic fuzzy set (IFS) theory can be used to to solve such problem. In this paper, models are proposed to determine intuitionistic fuzzy input-oriented mix-efficiency (IFIOME) with IF input and IF output data. For determining IFIOME, intuitionistic fuzzy input-oriented CCR (IFIOCCR) model and intuitionistic fuzzy input-oriented slack-based measure (IFIOSBM) model are proposed with IF input-output data. These models are solved by using expected values of intuitionistic fuzzy numbers (IFNs). Based on IFIOME, a ranking method is developed to rank the DMUs. Also, the intuitionistic fuzzy correlation coefficient (IFCC) between IF variables is proposed to validate the proposed models. To validate the proposed models, an illustrative example and a health sector application are presented.
Keywords: Data envelopment analysis; Intuitionistic fuzzy input-oriented CCR model; Intuitionistic fuzzy input-oriented SBM model; Intuitionistic fuzzy input-oriented mix-efficiency; Hospital efficiency.
Transient analysis of a Markovian N-policy queue with system disaster repair closedown setup times and control of admission
by T. Deepa, A. Azhagappan
Abstract: The main objective of this research work is to study the time-dependent behaviour of performance measures and probabilities for an M/M/1 queueing model with some interesting parameters such as closedown, setup periods, disastrous breakdown of the system, repair, N-policy and different control mechanism for the arrivals when the server is under repair as well as busy. In order to reduce the cost of production and to increase the profit, the manufacturing industries follow a technique of not to start the service until the number of work pieces reaches a fixed threshold value. Shutting down the machines when no jobs are available and setting up before the commencement of service play significant contributions to reach the goals in business organisations. The probabilities of the model under consideration are derived by the method of generating function for the transient case. Some system performance measures and numerical simulations are also presented.
Keywords: Markovian queue; N-policy; Disaster and repair; Closedown and setup times; Control of admission; Transient probabilities.
Analysis of state dependent M[X]/G(a, b)/1 queue with multiple vacation second optional service and optional re-service
by A. Azhagappan, T. Deepa
Abstract: The objective of this paper is to analyse an M[X]/G(a; b)/1 queueing model with second optional service, multiple vacation, state dependent arrival and optional re-service. After completing the first essential service, a batch of customers either requests for re-service or leaves the system without re-service. After the completion of first essential service (with or without re-service), the batch of customers either requests for second optional service or leaves the system. At the completion moment of the second optional service, the batch of customers either requests for re-service or leaves the system after the second service. Whenever the queue size is less than a, the server commences vacation. At the instant of vacation completion, if at least a customers wait for service, the server starts a busy period. Otherwise, the server resumes another vacation. Using supplementary variable technique, the steady-state probability generating function (PGF) of the queue size is obtained.
Keywords: Bulk queue; Second optional service; Multiple vacation; Optional re-service; State dependent arrival.
Analysis of self service (s,S) inventory with general lead time and positive service time
by Anoop N. Nair, Jacob M.J
Abstract: This paper deals with the analysis of (s, S) inventory with general lead time and positive service time in a self service environment. The time for serving the inventory is considered as the service time. Both the inter arrival times and service times are assumed to be exponential. Unsatisfied demands are lost. The transient and steady state analysis of such an inventory system are carried out. Explicit expressions for the Laplace transforms of the joint probability distributions of the number of demands and inventory size are obtained using supplementary variable technique. Important system characteristics and numerical results are also presented.
Keywords: Inventory; (s,S) inventory policy; Supplementary variable techniques.
Optimal deductible and coinsurance policies under mean-variance preferences
by Christopher Gaffney
Abstract: We present a mean-variance analysis of optimal insurance coverage, showing how the relationship between the attitudes of the insured, in the form of their risk tolerance level, and the insurer, in the form of the insurance premium, affects insurance demand. Optimal parameter values (deductible, coverage limit, coinsurance level, and stop-loss limit) are derived, and we show that policies which include coinsurance and either a stop-loss limit or a deductible reduce to a straight deductible policy in the optimum. We also show that straight coinsurance is inferior to these policies.
Keywords: Insurance; Deductible; Coinsurance; Optimal Coverage; Mean-Variance.
The effect of supply chain structure on inventory management: serial or parallel?
by Xiaoming Li, Qidong Cao, Thomas Griffin
Abstract: While the effect of lead time and its variability on the performance of supply chains continues to attract the attention of academicians and practitioners, the current research has been silent on how a topographic structural change of a supply chain simultaneously reduces both mean and variance of lead time. This study provides such an approach to concurrently reduce both mean and variance of lead time by a simple change on the supply chain structure from serial to parallel. We then show that such lead time reduction reduces mean and variance of lead time demand, which reduce cost in inventory management. We finally illustrate how our approach can be applied to more complex supply chains.
Keywords: lead time reduction; inventory management; supply chain management; serial and parallel; structural change.
Mathematical programming model to optimise an environmentally constructed supply chain: A genetic algorithm approach
by Rakesh Raut, Sejal Dhange, Vaibhav Narwane, Bhaskar Gardas, Balkrishna Narkhede, Niraj Dere
Abstract: The purpose of the study is to develop a network model for effective decision making from the sustainability aspect. The study proposes a mathematical programming model to optimise an environmentally constructed supply chain. The effect on the environment by components such as carbon monoxide, nitrogen dioxide and volatile organic particles formed during transportation in the supply chain has been considered. The multi-objective genetic algorithm optimises total cost and total environmental impact which were subjected to constraints of demand, return, flow balance, and capacity. The total cost consists of purchase cost, fixed cost, transportation cost, manufacturing cost, processing cost, and inventory cost. Environmental impact of production, transportation, handling, lead reclamation, and plastic recycling process was considered. The model also uses life cycle assessment-based method for quantification of environmental impact and establishes Pareto optimal solutions for minimisation of economic as well as environmental impact.
Keywords: Reverse logistics (RL); Closed-loop supply chain (CLSC); Environmental supply chain impact; Life cycle assessment (LCA); Battery Recycling; SLI Batteries; Multi-objective optimisation.
(s,S) Stochastic Inventory system in Jackson Network
by Md. Amirul Islam, Mohammad Ekramol Islam, Abdur Rashid
Abstract: In this work, we develop and analyze an (s,S) stochastic perishable inventory system at each node into Jackson network with a service facility in which the waiting hall for customers is of infinite size. Service times are exponentially distributed. We assume that demands arrive in the system according to a Poisson Process. Whenever the inventory level reaches the reorder level s an order Q units is placed to bring the level to S. The lead-time is exponentially distributed. The items of inventory have exponential life times. The joint probability distribution of the number of customers in the system and the inventory level is obtained in the steady state case. Matrix Analytical Method is applied to solve for the steady state occupancy probabilities. Various system performance measures in the steady state are derived. Numerical examples and graphical illustrations are provided to illustrate the proposed model.
Keywords: Jackson network; (s,S)-policy; Stability Condition; Performance analysis; Sensitivity Analysis.
A bi-objective robust possibilistic programming model for blood supply chain design in the mass casualty event response phase: A M/M/1/K queuing model with real world application
by Mohammad Mohammadi, Mahsa Pouraliakbari, Alireza Arshadi Khamseh, Bahman Naderi
Abstract: Effective and efficient supply of blood during and after disasters plays an important role to save the life of the victims. This paper presents a robust bi-objective blood supply chain design model for disaster relief considering four conflicting objectives simultaneously, as well as the uncertain nature of the supply chain. Minimisation of the total costs of the supply chain is considered as the first objective function to elevate the efficiency of the studied blood supply chain network. The tradeoff between the total expected waiting times of patients in hospitals and the average hospital idle-time probability is considered as the evaluation measures of the performance of hospitals in order to offer better services to patients.
Keywords: Disaster management; Blood Supply chain network design; Queuing theory; ?-constraint method; TH method; Waiting times; Robust possibilistic programming.
Combinatorial Artificial Bee Colony algorithm hybridized with a new release of Iterated Local Search for Job-shop Scheduling Problem
by Amaria Ouis Khedim, Mehdi Souier, Zaki Sari
Abstract: Job shop Scheduling Problem (JSP) is recognized as an attractive subject in production management and combinatorial optimization. However, it is known as one of the most difficult scheduling problems. The present paper investigates the job shop scheduling problem in order to minimize the Makespan with a new hybrid combinatorial artificial bee colony algorithm. Firstly, the proposed combinatorial version integrates a Position Based Crossover for the updating of solutions and the Rank-Based Selection for selecting solutions to be updated in the onlooker bees phase. Another purpose of this study consists to highlight the impact of its sequential hybridization with a new release of iterated local search method called Simple Iterated Local Search (SILS). The proposed approaches are tested on many benchmark problems taken from the Operations Research Library (OR-Library). The simulation results show that the hybrid CABC performs the best in most of the studied cases.
Keywords: Job shop Scheduling Problem (JSP); Metaheuristics; Artificial bee colony Algorithm; Iterated Local Search.
Solving Multi-objective linear fractional programming problem based on Stanojevics normalization technique under fuzzy environment
by Indrani Maiti, Tarni Mandal, Surapati Pramanik, Sapan Kumar Das
Abstract: Fuzzy linear fractional programming (FLFP) problem has always been a subject of keen interest, and a rigorous research has also been done on it. However, due to some limitation of these methods, they cannot be applied for solving multi-objective linear fractional programming (MOLFP) problem with fuzzy coefficients and fuzzy variables. To overcome these limitations, Taylor series approximation and normalisation technique is applied in this problem. In this paper, we deal with the concept of ?-cuts which are employed to defuzzify the problem. We also formulate the membership function of each objective function is linearised using first order Taylor series approximation and normalisation technique. Normalisation technique is employed to ensure that the range of the reduced membership function belongs to [0, 1]. Then fuzzy goal programming is applied to solve the formulated problem so that the negative deviational variables are minimised. Finally, the fruitfulness of the proposed algorithm is illustrated through numerical examples as compared to other method.
Keywords: Fuzzy number; Fuzzy goal programming; Multi-objective linear fractional programming problem; Taylor series; Normalization; Crisp functions.
AN EFFICIENT METAHEURISTIC FOR DYNAMIC NETWORK DESIGN AND MESSAGE ROUTING
by Robert Hartlage, Jeremy Jordan
Abstract: As information requirements continue to increase, faster algorithms are necessary to effectively and efficiently deliver critical information across the Global Information Grid Given a list of required message traffic, to include source, destination, size, and priority, the idea is to design networks to maximize the delivery of message traffic based on message priority and quality of service, and then route the messages efficiently. Due to the dynamic nature of the problem and the combinatorial explosion in size as new network nodes are added, a quick-running heuristic approach is needed. In this research, a metaheuristic is developed to dynamically design the network based on the projected message traffic requirements and efficiently route the required messages on the network, based on priority, maximizing the number of messages successfully delivered and the quality of service of the delivery. The meta-heuristic is tested and generates high quality solutions quickly relative to current methods.
Keywords: Metaheuristics; Network Flows; OR in military; OR in telecommunications.
AN INTEGRATED APPROACH FOR EVALUATING THE ENABLERS FOR GREEN MANUFACTURING USING DEMATEL AND ANALYTIC NETWORK PROCESS
by Sandeep Handa, Tilak Raj, Sandeep Grover
Abstract: In recent decades increase in environmental awareness has motivated the manufacturers towards minimizing the use of exhaustible resources. Green manufacturing focuses on manufacturing technologies and initiatives that optimize energy usage and resource conservation. Green manufacturing aims to minimize environmental impact of manufacturing activities. The central objective of green paradigm is the combination of economic and ecological efficiency. This study aims to identify the key enablers for green manufacturing. The study uses an integration of Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP). The analysis reflects the interdependencies among the enablers of green manufacturing. The results indicates that Customer demand, Implementing eco innovation and Availability of collaborative suppliers are the top three enablers for the transition towards green manufacturing.
Keywords: Green Manufacturing; Enablers; DEMATEL; ANP.
An exact approach to the integration of noncyclical preventive maintenance scheduling and production planning for a series
by NIZAR E.L. HACHEMI, Mohammed Anouar Jamali, Abdessamad AitElCadi, Louis Martin Rouseau, Abdel Hakim Artiba
Abstract: In this paper, we generalise a model for integrated noncyclical preventive maintenance scheduling and production planning from a single machine to a series-parallel production line. As for a single machine, we consider a set of products that must be produced in lots during a given time horizon. The maintenance strategy involves possible preventive replacements at the beginning of each maintenance period and minimal repair at machine failure. The model, an integer linear program, determines the optimal production plan and preventive replacement for each machine of the production line. The objective is to minimise the total cost (preventive and corrective maintenance costs, setup costs, holding costs, backorder costs, and production costs) while meeting the demand for each product over the horizon. We performed experiments using CPLEX 12.5.1, and almost all instances were solved within five minutes with a reasonable gap.
Keywords: Production planning; noncyclical preventive maintenance; linear programming; branching strategies; series-parallel system.
An innovative hybrid fuzzy TOPSIS based on Design of Experiments for multi criteria supplier evaluation and selection
by Mohammad Reza Marjani, Mohammad Habibi, Arash Arash Pazhouhandeh
Abstract: In this article, nine important criteria are considered to select the best supplier in supply chain risk management. For this purpose, to address the unspecified criteria and the results analysis, the combination approach of fuzzy TOPSIS and Design of Experiments (DOE) were presented and a 2k factorial design for factor analysis was used at two low and high levels. Combining the Fuzzy TOPSIS and Design of Experiments, gives the decision makers more freedom to select, because it can analyze the effects of different factors on the response variable by sensitivity analysis and according to different weights defined by each decision maker, obtain different results and compare them. In addition, for the ranking of the factors based on each response variable, the Pareto chart was used and the ineffective factors were eliminated. Finally, the ranking results for each decision maker were compared with Shannon entropy weight modification method and decision makers.
Keywords: supplier selection; supply chain risk management; fuzzy TOPSIS; design of experiments; DOE; 2k factorial design; Pareto chart; Shannon entropy; Analysis of variance; ANOVA; grey TOPSIS.
USING CRITERION BASED MODEL AVERAGING IN TWO-INPUT MULTIPLE RESPONSE SURFACE METHODOLOGY PROBLEMS
by Domingo Pavolo, Delson Chikobvu
Abstract: Experimental designs in multiple response surface methodology (MRSM) often result in small sample size datasets with associated modeling problems. Classical model selection criteria are inefficient with small sample size datasets and sample sizes below (10+k), where k is the maximum number of regressors inclusive of the intercept, suffer from credibility while the model selection process has inherent uncertainty. In this empirical paper, criterion based frequentist model averaging (CBFMA) is investigated as a solution to the problems of modeling MRSM datasets. We also compare the accuracy of process optimisation using CBFMA models versus ordinary least squares (OLS) candidate models. Findings suggest CBFMA models produce effective and accurate results and solve the small sample size model selection criteria bias problem. However, in the MRSM context, CBFMA does not directly solve the model selection uncertainty problem and averaged model estimators have mean squared errors that are greater than the best OLS candidate models.
Keywords: Multiple Response Surface Methodology; Experimental design; All Possible Regression Models; Frequentist Criterion Based Model Averaging; Small Sample Size Datasets; Process optimisation.
Application of a Mobile Facility Routing Problem in a delivery company
by Sonia Avilés-Sacoto, Osmar Salvador-Grijalva, Galo Mosquera-Recalde
Abstract: Nowadays e-commerce business has created new distribution channels to reach customers. This setting includes policies and changes in the delivery process, such as small orders, short delivery schedules, and variables workloads. One approach to meet these requirements is mobiles facilities, which place products closer to customers with known location and product requirements. However, finding out the right position for the mobile facilities considering time and demand variability constitutes a challenge. This study proposes a methodology to determine the location and time where mobile facilities should be to optimize the delivery of products. Several tools such as clustering, routing problem optimization, and Monte Carlo simulation are combined with the aim to minimize the transportation costs associated to the delivery of the products and the cost associated to the unmet demand. A real case study is presented with the optimal route for two mobile facilities for a 15-minutes time window.
Keywords: Mobile Facility; Routing Problem; Monte Carlo Simulation; Clustering; Delivery; Transportation; Costs; Vehicles; Demand; Strategic Points; Time window.
An EOQ model for non-instantaneous deteriorating items with time-dependent quadratic demand and two-level pricing strategies under trade credit policy.
by Babangida Bature, Yakubu Mamman Baraya
Abstract: In this paper, an EOQ model for non-instantaneous deteriorating items with two phase demand rates and two-level pricing strategies under trade credit policy is considered. It is assumed that the unit selling price before deterioration sets in is greater than that after deterioration sets in. Also, the demand rate before deterioration sets in is assumed to be continuous time-dependent quadratic and that after deterioration sets in is considered as constant and shortages are not allowed. The main purpose of this research work is to determine the optimal cycle length and corresponding economic order quantity such that the total profit of the inventory system is optimise. The necessary and sufficient conditions for the existence and uniqueness of the optimal solutions are established. Numerical examples are given to illustrate the theoretical result of the model, Sensitivity analyses of some model parameters on the decision variables were carried.
Keywords: economic order quantity; non-instantaneous deteriorating items; time-dependent quadratic demand rate; two-level pricing strategies; trade credit policy.
Hierarchical learning model for early prediction of coronary artery atherosclerosis
by Sowmiya M.
Abstract: Drastic improvement in the field of science and technology have made the lives of humans more sophisticated. As a result, physical activities in which the people indulged have reduced and this has made them prone to Coronary Artery Disease (CAD). Coronary Artery Atherosclerosis (CAA) is one of the main causes of CAD and therefore, early prediction of CAA is indispensable to prevent the risk of people getting affected by CAD and sudden deaths. This work presents the machine learning model which provides more information on the exceptional cases while retaining the existing traditional classifier model. The proposed model performs outliner detection using Local Outlier Factor (LOF) and class balancing using Synthetic Minority Oversampling Technique (SMOTE). Genetic algorithm is used for prominent feature selection and utilizes Support Vector Machine (SVM) and Neural Network (NN) as classifier. UCI and South African Heart disease datasets are used to implement the proposed model.
Keywords: Machine learning; support vector machine; neural network; local outlier factor; feature selection.
Octanary Polyhedral Branch and Bound for Integer Programs
by James P. Bailey, Todd Easton, Fabio Vitor
Abstract: This paper introduces the octanary branching algorithm (OBA), a polyhedral branching technique to solve integer programs. Unlike the traditional branch and bound algorithm, each of OBA's branching nodes generates eight children instead of two. Four of them are created by equality constraints, while the other four use inequalities. This branching strategy allows a dimension reduction of the linear relaxation space of the four equality children, which should enable OBA to find quality integer solutions sooner than the branch and bound algorithm. Computational experiments showed that the branch and bound algorithm required over one billion nodes to identify a solution that is at least as good as the solution found by OBA after only half a million nodes. Consequently, OBA should replace the branch and bound algorithm during the first portion of the branching tree, be used to identify a warm start solution, or be implemented as a diving strategy.
Keywords: Branch and Bound; Hyperplane Branching; Branching Polyhedra; Random Diving; Integer Programming.
Fuzzy multi-objective programming for supplier portfolio formation: A credibility based approach
by Garima Mittal
Abstract: In a multiple-sourcing environment, supplier selection plays an integral role in efficiently managing a supply chain of an organization. This paper proposes a fuzzy multi-objective programming model using a credibility measure, for forming a portfolio of suppliers. Credibility measure has an advantage of self-duality over more prevalent possibility measure. In this paper credibility measure of a fuzzy event is used to model the three objectives of minimizing expected cost, maximizing expected quality and maximizing the expected on-time delivery, along with other realistic constraints that are integral part of the supplier selection problems. The proposed credibilistic model is solved using a fuzzy goal programming approach that has then been extended to three different scenarios in order to address decision maker's satisfaction with respect to the achievement level of the various objectives. A numerical illustration is also provided to depict the utility of the proposed model and approach.
Keywords: Fuzzy supplier portfolio; Expected value model; Fuzzy multi-criteria decision making; Credibility measure; Fuzzy goal programming; Supplier selection.
A Multiple Criteria Decision Making improvement strategy in complex manufacturing processes
by Soltani Mohyiddine, AOUAG Hichem, Mouss Mohamed Djamel
Abstract: The purpose of this paper is to propose an improvement strategy based on multi-criteria decision making approaches, including fuzzy analytic hierarchy process (AHP), preference ranking organisation method for enrichment evaluation II (PROMETHEE) and viekriterijumsko kompromisno rangiranje (VIKOR) for the objective of simplifying and organising the improvement process in complex manufacturing processes. Firstly, the proposed strategy started with decision makers selection including leaders company to determine performance indicators. Than fuzzy AHP is used to quantify the weight of each defined indicators, finally, the weights carried out from fuzzy AHP approach are used as input in VIKOR and PROMETHE II to rank the operations according to their improvement priority. The results obtained from each outranking method are compared and the best method is determined.
Keywords: analytic hierarchy process (AHP); VIekriterijumsko KOmpromisno Rangiranje (VIKOR); Fuzzy Logic; PROMETHE; improvement priority; Complex manufacturing process.
Multi-Criteria Optimization of Fire Station Location in Gaza Strip
by Abdelrahman Abuserriya, Sadiq Abdelall, Salah Agha
Abstract: Fire stations along with their locations in Gaza Strip are extremely important because of the political situation This paper evaluated fire stations locations and suggested how to improve their performance in Gaza Strip The paper uses two models, these models include set covering and goal programing models, the models were solved in two stages: the first stage used the set covering model to determine the number of fire stations needed for given time limits (6, 8 minutes) The output of the first model was inputted into second model to determine the locations of fire stations Further, several scenarios were proposed for sensitivity analysis The first scenario freely identified the locations of each fire station, while the second scenario was forced model to select/unselect given locations based on decision makers preferences Based on fire department request which called for expansion through identifying the optimum number and locations of new fire stations.
Keywords: fire stations location; goal programing; Gaza Strip; multi criteria; set covering.
Optimal Production Output and Frequency of Rest-Breaks for a Worker Subjected to Fatigue
by Mohammed Darwish, Khaled Alali, Adel Alshayji
Abstract: It is reported by many researchers that the quality of produced items deteriorates and work-related incidents increase when a worker is subjected to high level of fatigue at workplace. As a direct consequence, the daily production yield of the worker decreases which, in turn, reduces the profit of a company. Some authors suggested short breaks at workplace so that the worker overcomes fatigue and improve productivity. In this paper, we mathematically study this relationship and develop a model that finds the optimal work-rest schedule such that the daily production output of a worker is maximized. We assume that the worker fatigue increases exponentially during work. Unlike the models used in the literature, the reduction in the worker fatigue level is modeled by utilizing concepts in the preventive maintenance engineering literature. This simplifies the solution method significantly.
Keywords: Operations research; production; worker safety; rest-breaks; worker fatigue; exponential fatigue function.
BRANCH AND BOUND TECHNIQUE FOR TWO STAGE FLOW SHOP SCHEDULING MODEL WITH EQUIPOTENTIAL MACHINES AT EVERY STAGE
by SONIA GOEL, Deepak Gupta
Abstract: This paper is an attempt to schedule n-jobs on two machines with parallel equipotential machines at every stage. The operating costs of all jobs on all parallel equipotential machines are given. The dispensation time of all the jobs on all the two machines is given and the time for which parallel equipotential machines are available is also known. The purpose of this work is to find the best possible schedule of jobs with the intention to diminish the entire elapsed time. The algorithm developed is illustrated with the help of example.
Keywords: Scheduling; Elapsed time; equipotential machines; operating cost.
Optimizing Fully Fuzzy Interval Integer Transshipment Problems
by A. Akilbasha, Pandian Ponnaiah, Natarajan G
Abstract: In this paper, we focus on solving fully fuzzy interval integer transshipment (FIIT) problems where the unit shipping costs, available supply capacities, and required destination demands are triangular fuzzy interval integers. An innovative method namely, back order sequence method has been developed for finding an optimal solution of the fully FIIT problem. The proposed method provides that the optimal values of decision variables and objective function value for the fully FIIT problem are fuzzy interval integers. A numerical example is presented to illustrate the solution procedure of optimizing fully FIIT problems. The optimal solution to the transshipment problem by the proposed method can help the managers to take an appropriate decision regarding transshipments.
Keywords: Interval; Fuzzy set; Fuzzy interval; Transshipment problem; Optimal solution; Back order sequence method.
In-House Production vs Outsourcing: the Effect of Volume-Based Learning on Quality Competition
by Yanni Ping, Seung-Lae Kim
Abstract: This paper considers an original equipment manufacturer (OEM) who outsources finished products to a contract manufacturer (CM), who adopts the OEMs existing technology and achieves quality improvement through learning-by-doing. Besides the role of upstream partner, the CM also becomes a downstream competitor. We examine learning-by-doing and quality dynamically under a two-period model both for cases when quality competition exists and does not exist. We identify the conditions under which pure outsourcing, partial outsourcing, or non-outsourcing is most advantageous. When there is no quality competition and when the CMs quality improvement does not hurt the OEMs future demand, we find that it would still be beneficial for the OEM to apply a partial outsourcing strategy. When quality competition exists, the OEMs decision in the second period follows the same pattern as the non-competition case, while the CMs wholesale price depends on the trade-off between selling through the OEM and selling independently.
Keywords: learning-by-doing; outsourcing; quality-improvement; competitive CM.
Research on Optimal Product Supply Strategies for Manufacturer-to-Group Customer under a Real Demand Pattern
by Zhiyi Zhuo
Abstract: In a product supply chain, customer demand determines the market and supply as well as the benefits accruing to manufacturers and retailers. Customer demand has three different patterns: real, false, and semi-real. This paper develops a mathematical model to investigate the factors that determine manufacturers design and plan of product supply strategies for group customers under the real demand pattern and solve for maximum profit. We use numerical examples to verify the validity of the model. This papers contribution is the construction of two mathematical models for off-invoice mode and unsold recycling mode of manufacturer-to-group customer product supply strategies under a real demand pattern.
Keywords: Real demand pattern; Product supply strategies; Profit function; Optimal method.
A Complete Information PCA-Imprecise DEA Approach for Constructing Composite Indicator with Interval Data: an Application for Finding Development Degree of Cities
by Hashem Omrani, Kolsoom Zamani
Abstract: Composite indicator approach is widely used for finding development degree of regions.This paper presents a complete information principal component analysis (CIPCA)-Imprecise data envelopment analysis (IDEA) approach for finding development degree of cities with uncertain data. CIPCA is applied to reduce the number of indicators. The output of the CIPCA is a set of new indicators with lower and upper bounds. These indicators are considered as indicators of IDEA and final ranks of cities are calculated by IDEA model. To illustrate the capability of CIPCA-IDEA approach, the development degrees of cities in Kurdistan province of Iran are calculated. First, 62 development indicators are selected and the related interval data for year 2015 are gathered. Then, the proposed approach is applied for nine cities of Kurdistan province and development degree for each city is finally calculated. The results indicate that in the overall ranking, Bijar city is top ranked.
Keywords: Composite indicator; CIPCA; IDEA; Development degree.
Development of a Heuristic for No-Wait Flowshop Production Process to Minimize Makespan
by Azahar Alam
Abstract: No-wait flow shop production system has much practical application in manufacturing industries. Minimisation total completion time of no-wait flow shop comes under the NP-complete category. Consequently, getting effective and efficient quality of result for such types of problems is a challenge where effective means optimal or near-optimal solution and efficient means which taking less computational time. A heuristic is proposed for the problem under consideration, in which problem is getting solved in two phases. The first phase is the initial sequence generation, and the second one is the improvement phase. Proposed heuristic is compared with four best-known heuristics, and for smaller size problem exhaustive enumeration is used to check the performance of the heuristic. Moreover, heuristic performance is tested over 600 randomly generated problem of different sizes. For large size problem, it showed that proposed heuristic is very much efficient.
Keywords: Heuristic; No-wait flowshop problem; Makespan minimisation; Computational complexity.
Optimizing Production Plan for Underground Coal Mining: A Genetic Algorithm Application
by Supriyo Roy, R.P. Mohanty
Abstract: Developing an optimal production plan of an underground coal mine is complex due to several factors such as: economic, physical, environmental and social, etc. In this paper, an attempt has been made to apply genetic algorithm (GA) to maximise net present value (NPV) of a real life underground coal mine. It is first highlighted that the inefficacy of using direct optimisation methods and then a numerical illustration shows the efficacy of application of bio-inspired computation approach; because of its multiple advantages such as simplicity, user friendliness and parallel processing. This paper establishes the proposition that simulation-based stochastic optimisation for underground mine production plan would lead to better results than optimisation based on customary gradient optimisation approach.
Keywords: Underground Coal Mining; Production Planning; Optimization; Evolutionary Search; Genetic Algorithm.
JACKKNIFING THEN MODEL AVERAGING: Investigating the Improvements to Fitness to Data and Prediction Accuracy of Two-Input Under- and Just-Fitted Response Models
by Domingo Pavolo, Delson Chikobvu
Abstract: The possibility of improving the fitness to data and prediction accuracy of models in a multi-response surface methodology environment of under and just-fitted ordinary least squares response models by jackknifing then combining the resultant partial estimates and the pseudo-values using arithmetic averaging or criterion-based frequentist model averaging was investigated. Jackknifing is known to reduce parametric and model bias. Model averaging is known to reduce model bidirectional bias and variance. A typical multi-response surface methodology dataset and resultant validation dataset were used as example. Results suggest that it is possible to obtain better fitness to data and prediction accuracy by jackknifing a just-fitted response model of interest and combining the resultant partial estimates using arithmetic averaging. The combining of pseudo-values using arithmetic averaging or criterion-based frequentist model averaging gave mixed results. The actual jackknife model estimators gave good performance with under-fitted models.
Keywords: KEY TERMS: multiresponse surface methodology; jackknifing; partial estimates; pseudo-values; arithmetic model averaging; criterion-based frequentist model averaging.
A Lower Bound Competitive Ratio for the Online Stochastic Shortest Path Problem
by Mohsen Abdolhosseinzadeh
Abstract: In the online networks, some parameters are not known for decision makers in prior, especially the arc costs are revealed over time; so, the online decisions should be made without complete knowledge of the future events. Three kinds of statistical information are available by arrival the last traversed nodes in an online manner: the exact traversed length, the average shortest path length and the shortest path length. So, three different stochastic models are considered and the related stochastic online decision criteria are obtained, such that the best competitive ratio is 2.3130. Again, it is assumed that the online decision maker is informed about the intervals of the arc costs; then, some constant competitive ratios are produced and 2.3130 determined as the best obtained lower bound competitive ratio against some previous works.
Keywords: Online stochastic network; Online decision problem; Competitive analysis; Online stochastic shortest path.
Production Planning and Scheduling with Applications in the Tile Industry
by Armindo Soares, Carina Pimentel, Ana Moura
Abstract: In this paper we consider the medium to short term production planning and scheduling (PPS) process of a ceramic tile industry. The PPS process encompasses three problems: (1) the development of a master production plan that determines the medium-term production needs; (2) the development of a biweekly production scheduling plan that minimizes the production time required to complete the set of products, so as to meet customer orders within agreed due dates and ensure the filling of connected firing kilns; and (3) the available-to-promise problem. The production scheduling problem (PSP) was addressed as an identical parallel machine problem, with machine eligibility constraints, family and subfamily setups and minimum production lot sizes. A specific heuristic and a mixed integer programming model are proposed to solve the PSP. A model-driven decision support system, that improves the quality and time expenditure of the PPS process, is also presented.
Keywords: production planning and scheduling constructive heuristic decision support system mixed integer programming tile industry master production schedule available to promise production scheduling.
Detection of the Diffusion Nanoparticle in the Turbulent Flows Using the Random Walk Model
by Mohamed El-hadidy
Abstract: A probabilistic detection model is proposed to determine the location of random walk nanoparticles in the turbulent flows. This model maximises the benefits for the environmental, industrial and biological applications. In this paper, we present a generalised coordinated linear detection technique to find a one-dimensional random walk nanoparticle between two layers in turbulent flows. We have two sensors start the searching process from any point rather than the origin. The initial position of the nanoparticle is a random variable that has a known probability distribution. More than showing the existence of a finite search plan, we derive some auxiliary results related to optimal search plan which minimises the expected value of the first meeting time between one of the sensors and the nanoparticle. A numerical example is provided to illustrate the effectiveness of this technique.
Keywords: Turbulent flows; Random Walk nanoparticle; Probability space.
Solving the Team Orienteering Problem with Time Windows and Mandatory Visits Using a Constraint Programming Approach
by Ridvan Gedik, Emre Kirac, Furkan Oztanriseven
Abstract: This paper presents a constraint programming (CP) approach for solving the team orienteering problem with time windows and mandatory visits (TOPTW-MV), which has many real-world implementations, such as tourist tour planning, routing technicians, and disaster relief routing. In the TOPTW-MV, a set of locations is given; some locations must be visited, while others are optional. For each location, the profit, service time, and service time window information are known. A fleet of homogeneous vehicles is available for visiting locations and collecting the profits. The objective in solving this problem is to create a set of vehicle routes that begin and end at a depot, visit mandatory locations exactly once and optional locations at most once, while observing other restrictions such as time windows and sequence-based travel times. The CP-based approach finds 99 of the best-known solutions and explores 64 new best-known solutions for the benchmark instances.
Keywords: Team orienteering problem; time windows; mandatory visits; vehicle routing; constraint programming; CP; optimization.
Distribution and inventory planning in multi-echelon supply chains under demand uncertainty
by Joaquim Jorge Vicente, Susana Relvas, Ana Barbosa-Póvoa
Abstract: Distribution and inventory planning in a multi-echelon system are studied under an uncertain demand context. To deal with this problem a mixed integer linear programming (MILP) model is proposed. This considers a multi-echelon system formed by N-warehouses and M-retailers. The problem consists on determining the optimal reordering plan for the operating network, which minimises the overall systems operation cost. The uncertain demand faced by retailers is addressed by defining the optimal safety stock that guarantees a given service level at each regional warehouse and each retailer. Also, the risk pooling effect is taken into account when determining inventory levels in each entity. A case study based on a real retailer distribution chain is presented and solved.
Keywords: supply chain management; inventory planning; mixed integer linear programming; MILP; guaranteed service approach; demand uncertainty; risk pooling.
A tri-level mixed-integer program for the optimal fortification of a rail intermodal terminal network
by Manish Verma, David M. Tulett, Hassan Sarhadi
Abstract: Rail-truck intermodal transportation plays an important role in moving freight in North America, and hence the availability and appropriate functionality of the associated infrastructure is crucial. To this end, one of the strategies to mitigate (minimize) the adverse impacts from (intentional/random) disruption prescribes fortifying a set of rail intermodal terminals so that continuity in its service is ensured. In this paper, we re-visit the tri-level model to protect a given number of rail intermodal terminals such that the effect of worst-case disruptions is minimized. We propose a tabu search metaheuristic to solve the outer level problem, and then combine it with a decomposition-based technique to solve the entire model. The proposed methodology is tested on the problem instances introduced in an earlier work, and to comment on the computational efficiency vis a vis the existing techniques in the literature.
Keywords: intermodal transportation; mixed-integer program; fortification; tabu-search metaheuristic; decomposition.
The problem of detecting nonlinearity in time series generated by a state-dependent autoregressive model. A simulation study
by Fabio Gobbi
Abstract: The aim of the paper is to try to measure, through a Monte Carlo experiment, nonlinearity in time series generated by a strictly stationary and uniformly ergodic state-dependent autoregressive process. The model under study is intrinsically nonlinear but the choice of parameters strongly impacts on the type of serial dependence making its identification complicated. For this reason, the paper exploits two statistical tests of independence and linearity in order to select the parameter values which ensure the joint rejection of both hypothesis. After that, our study uses two measures of nonlinear dependence in time series recently introduced in the literature, the auto-distance correlation function and the autodependence function, in order to identify nonlinearity induced by the proposed model.
Keywords: nonlinear time series; independence test; linearity test; auto-distance correlation; autodependogram.
Real Coded Self-Organizing Migrating Genetic Algorithm for nonlinear constrained optimization problems
by Avijit Duary, Nirmal Kumar, Md. Akhtar, Ali Akbar Shaikh, Asoke Kumar Bhunia
Abstract: The objective of this article is to propose a new hybrid algorithm named as real coded self-organising migrating genetic algorithm (C-RCSOMGA) by combining real coded genetic algorithm (RCGA) and modified self-organising migrating algorithm (SOMA) for solving the nonlinear constrained optimisation problems. In RCGA, a modified mutation operator called as double mutation operator has been introduced combining two different existing mutation operators, whereas in SOMA, a modified strategy has been proposed. To test the performance of the proposed algorithm, a set of test problems taken from the existing literature has been solved and the simulated results have been compared numerically as well as graphically with the existing algorithms. In the graphical comparison, a modification of performance index (PI) has been made. Finally, with the help of modified performance index (MPI), it has been shown that the proposed hybrid algorithm has performed much better than the existing algorithms.
Keywords: genetic algorithm; self-organising migrating algorithm; SOMA; performance index; nonlinear constrained optimisation; global optimisation.
Aggregate Production Planning of Abu Ghraib Dairy Factories based on Forecasting and Goal Programming
by Wakas Khalaf
Abstract: The aim of this article is to build a comprehensive multi-objective production plan for Al-Rafidain plant spanning 12 months based on two methods the auto regression integrated moving averages (ARIMA) model to forecast the market demand for the products and the method of goal programming (GP) to find compatible solutions among the goals to be achieved. The ready-made program MATLAB was used to find the future values of the time series and also to solve the multi-objective mathematical model. For the most important results achieved, the mathematical model was able to achieve the first goal by 97%, which was the maximisation of profits; the total profits achieved a value of USD3,625,856. The second goal was achieved successfully because of the decrease that occurred in the costs, the value of which was USD2,456,625. Finally, the third goal was achieved by 98%, in that the plants return on investment was decreased to 1.476.
Keywords: aggregate production planning; APP; goal programming; forecasting; auto regression integrated moving averages; ARIMA.
Multi-objective optimization for solving cooperative continuous static games using Karush
by PAVAN KUMAR, Hamiden Abd El- Wahed Khalifa
Abstract: This paper introduces cooperative continuous static games (CCSG) with parameters in the cost functions of the players and in the right-hand side of the constraints. The CCSG is converted into the corresponding multi-objective nonlinear programming problem. The resulted nonlinear programming problem is converted into the single objective nonlinear programming problem through the use of the weighted sum method. A solution method for obtaining the stability set of the second kind without differentiability for the CCSG is presented using Karush-Kuhn-Tucker conditions. A numerical example is given for the illustration.
Keywords: Cooperative continuous static games; Efficient solution; Weighted sum method; optimal solution; Karush-Kuhn-Tucker conditions; Stability.
Optimization of finite Economic Production Quantity Model Under Cloudy Normalized Triangular Fuzzy Number
by Neelanjana Rajput, R.K. Pandey, Anand Chauhan
Abstract: This study introduced economic production quantity (EPQ) model with a finite production rate is established for cloudy normalised triangular fuzzy number (CNTFN). In real-life situations, the goals and inventory parameters are may not precise. Such type of uncertainty may be characterised by fuzzy numbers. The main object of this research effort is to develop a mathematical model and optimise EPQ with different environment like crisp, general fuzzy and cloudy fuzzy situations. A novel defuzzification methodology has been used for EPQ by Yagers ranking index method. Here, the constraint goal and the inventory cost parameters are assumed to be triangular-shaped fuzzy numbers with different types of left and right membership functions. The cost functions associated to these models are verified to be convex and optimal criteria are established in all three situations. The models are numerical, graphically demonstrated and sensitivity analysis shows a decent explanation. Also, discuss the applications and future scope of the CNTFN model in realistic situations such as when items are not easy to replenish due to some transport problem and some problems in geographically hilly regions, how to use cloudy fuzzy number
in that situations.
Keywords: fuzzy optimisation; decision making; cloud fuzzy number; EPQ inventory model; finite production; extended Yager’s ranking index method.
Misspecification of Data Envelopment Analysis
by KEKOURA SAKOUVOGUI
Abstract: Data envelopment analysis (DEA), a non-parametric efficiency estimator uses linear programming technique for the computation of estimates of decision-making units, such as, universities, schools, hospitals, banks, or mutual funds. There has been an ongoing debate about the application of the DEA model for model misspecification and in particular the inefficiency error of production for input and output variables. This paper contributes to this debate by examining several misspecifications of the DEA model in Monte Carlo (MC) simulations. MC simulations are conducted to examine the performance of the DEA model under two different data generating processes, stochastic and deterministic, and across five different misspecification scenarios, inefficiency distributions (traditional and proposed approaches), sample sizes, production functions, input distributions, and curse of dimensionality.
Keywords: data envelopment analysis; DEA; inefficiency distributions; Monte Carlo simulations.
Markov manpower planning models: a review
by Virtue Ekhosuehi, Vincent A. Amenaghawon, Augustine Osagiede
Abstract: A manpower system is a network of individuals working together in an organisation for the purpose of achieving the common goal of the organisation. To ensure that the right number of individuals is available to meet the task to be performed by the organisation, manpower planning techniques are needed. This paper reviews the manpower planning literature with specific interest on manpower systems modelled within the Markov chain context. Markov chains provide a convenient framework to: analyse the structural mechanisms, which underlie social change, and extrapolate shifts in the state distribution of the system; control personnel structures by framing optimal promotion and recruitment strategies; evaluate personnel policies; and deal with heterogeneity and uncertainty in the system configuration. This paper surveys these applications areas and highlights the methodological issues arising from varying the unit interval of the Markov manpower system in discrete time.
Keywords: embeddability problem; heterogeneity; manpower planning; manpower system; Markov chain; personnel structure; stochastic matrix; sub-stochastic matrix; transition matrix.
Agent-based modelling in Capacitated Lot Sizing Problem with Sequence dependent Setup time
by P. Raghuram
Abstract: Setups are indispensable in production, but consume substantial amount of productive time It is vital to consider sequence dependent setup times for determining production lots to satisfy demand for diversified product types on parallel production lines Optimisation is the most used technique to generate detailed schedules for such sequencing problems But the feasibility of the solution is not guaranteed under uncertainty conditions Thus, evaluating the solution configurations under uncertainty is necessary to confirm feasibility In this paper, an agent-based, discrete event simulation technique is used to develop a flowshop model which faces demand for multiple product varieties, and has sequence dependent setup time The simulation aims at evaluating various results in the solution space of an optimisation model to check their feasibility under various uncertain conditions in the form of setup time, processing time, and demand Results indicate that uncertainty influences overtime cost, holding cost, and lost sales.
Keywords: Agent-based modelling; capacitated lot sizing model; flow shop scheduling; optimization; simulation.
THE USE OF MOBILE APPLICATION TO BUY INSURANCE: AN AHP BASED STUDY
by Lav Ishan
Abstract: The Indian insurance industry is moving towards integrating the latest technology in their business activities, as this can help in increasing the companys efficiency. The use of mobile is high among the current generation, using this platform by insurance companies as a part of their business activities can help the company in many ways. This paper focuses on the use of mobile software by the customer and what are the factors which drive its use. Quantitative data were collected using questionnaire and interview of the respondent was taken to get an in-depth perspective regarding the mobile application. It was found out that the mobile application of insurance companies is used less by the customers and the way of increasing the use of the application is discussed. Findings can help the companies in determining how they can popularise their companys mobile application among their existing and targeted customers.
Keywords: Indian Insurance Industry; Insuretech; Multi-Criteria Decision Making; Analytical Hierarchal Process.
Multi-resource balancing: A case of a German kitchen manufacturer
by Sina Glaeser
Abstract: We address a multi-resource balancing-problem at Nobilia-Werke J. Stickling GmbH & Co. KG, the leading kitchen manufacturer in Germany. Nobilia maintains no warehouse for finished goods. The customised cabinets of a kitchen are manufactured in parallel and aggregated just in sequence for loading into trucks immediately after production. A smooth flow of materials is essential to ensure on-time completion and loading of a whole kitchen. Nowadays, changing customer tastes drive customisation. Due to an increasing number of capacity restrictions in its production processes, Nobilias current planning method has reached its limits. We present an integer programming model to address a variant of the multi-resource generalised assignment problem (MRGAP). We consider a multi-criteria objective function and a set of constraints reflecting Nobilias requirements. We propose a software-based solution approach for Nobilias instance sizes. The results of our computational experiments on real-world data demonstrate that our approach provides significant benefits.
Keywords: resource balancing; integer programming; multi-criteria; production planning; machine assignment; mass customisation; generalised assignment problem; GAP; real-world application; LINGO.
Numerical Optimization of Loss System with Retrial Phenomenon in Cellular Networks
by Vidyottama Jain, Raina Raj, Dharmaraja Selvamuthu
Abstract: In this study, we extend upon the model by Haring et al. [IEEE Trans. Veh. Technol. 50, 664-673 (2001)] by introducing retrial phenomenon in multi- server queueing system. When at most g number of guard channels are available, it allows new calls to join the retrial group. This retrial group is called orbit and can hold a maximum of m retrial calls. The impact of retrial over certain performance measures is numerically investigated. The focus of this work is to construct optimization problems to determine the optimal number of channels, the optimal number of guard channels and the optimal orbit size. Further, it has been emphasized that the proposed model with retrial phenomenon reduces the blocking probability of new calls in the system.
Keywords: Multi-server queueing model; retrial phenomenon; cellular network; blocking probability; optimization.
A Strategic Donor-Beneficiary Assignment Problem under Supply and Demand Uncertainties
by JYOTIRMOY DALAL
Abstract: Considering the coexistence of food insecurity and food waste issues in the society, in collaboration with an NPO in India, we develop a novel donor-beneficiary strategic assignment model to connect the students of a set of volunteering schools with a set of habitats with under-nourished children up to 14 years of age, via a social-awareness-raising long-term endeavor. Our two-stage stochastic programming model, by addressing the demand- and supply uncertainties using discrete scenarios determines optimal strategic connections to minimize the strategic connection cost as well as the expected shortage cost at the habitats due to unmet demand. We present a small but realistic test case, conduct a sensitivity analysis to illustrate the underlying trade-offs among various components and highlight how the decision-maker can adjust system-flexibility by altering certain model parameters.
Keywords: Food insecurity; food waste; donor; beneficiary; strategic assignment; supply and demand uncertainty; stochastic programming; scenario; mixed-integer programming model; non-profit organization.
Efficiency Analysis of Selected Bank-Sponsored Mutual Fund Schemes in India
by RAJIB DEB, Soma Panja
Abstract: There has been growing literature related to performance analysis of the mutual fund (MF) industry concentrating on the non-parametric approach. Because of the practical and academic importance of mutual funds, it is becoming a vital area of research in finance. The present study seeks to gauge the efficiency of 40 equity mutual fund schemes that belong to seven asset management companies categorised as bank-sponsored under AMFI in India. The study uses data envelopment analysis technique as the prime tool for knowing the efficiency of selected schemes. The study discovers only seven schemes as efficient out of total sampled schemes and in which Canara Robeco Asset Management Co. Ltd offers the maximum number of efficient schemes as compared to others. The present study will offer direction for future research and will also provide individual investors with a guideline for selecting funds.
Keywords: bank-sponsored mutual funds; data envelopment analysis; performance efficiency.
Improved Memetic Programming algorithm
by Souhir Elleuch, Bassem Jarboui
Abstract: Automatic programming is an efficient technique that has contributed to an important development in the field of artificial intelligence. Genetic Programming (GP) is a well known automatic programming algorithm based on genetic algorithm and evolves programs. In the present paper, we propose a new automatic programming method called two-dimensional Memetic Programming. It combines GP with local searches. We also introduce a new program representation for automatic programming algorithms. For this reason, the Memetic programming algorithm is extended to evolve this program specific structure. To show the effectiveness of our method, we tested it on benchmark problems drawn from time series prediction and medical datasets classification, and we compared it with the related techniques.
Keywords: Automatic Programming; Memetic Programming; Local Search; Time-series forecasting; Classification.
Comparison of platoon formations using a departure time coordination heuristic
by Gajanand M. S, S. Sivanandham
Abstract: A platoon is a set of virtually linked vehicles that drive closely behind one another. Truck platooning is a fast-emerging application of connected and autonomous vehicles to help tackle the traditional problems of the transportation industry to reduce cost and minimise emissions. In this study, we present a departure time coordination based heuristic solution for platoon formations. We also use a comprehensive modal emission model to understand the influence of velocity and size of the platoon on fuel savings potential. We compare the heuristics performance on the different variants of the platooning problem in the literature with a central platoon coordinator and analyse the impact of the type of vehicle, speed, load and size of the platoon on the fuel savings potential of platooning. Numerical analysis shows a fuel savings potential of 6.4% to 8% for a platoon size of three with Heavy Duty Vehicles at 80kmph for different configurations.
Keywords: Platooning; Fuel Savings; Routing Problem; Departure Time; Platoon Formation; Freight Transportation.
Categorization of offshore wind production system
by Jon Lerche, Hasse Neve, Søren Wandahl, Allan Gross
Abstract: This paper investigates and describes the production system characteristics for offshore wind turbine assembly and compares it to manufacturing and construction. It requires understanding of the organizations, products and processes to categorize the production system for offshore wind farm assembly. This study compares explorative cases with literature describing the production system characteristics from production and manufacturing domains. The categorization is completed by comparing product-process first and then product-organization. To understand the identified differences, the results are displayed in matrixes with manufacturing and construction characteristics from the literature, compared with field notes, observations and archived data from wind turbine assembly cases. The results show offshore wind turbine assembly as a hybrid production system in this comparative study. The comparison contributes to the novel understanding of the wind industry and is theory building within operations research. It establishes a baseline for further operational research within the offshore wind domain.
Keywords: Construction; Comparison; Lean; Offshore Wind; Operations management; Production system theory.
Simulation-Based Optimization Approach to Multi-Choice Transportation Problem
by N.Tuba Y?lmaz Soydan, Ahmet Mete Çilingirtürk
Abstract: The classical transportation problem assumes that freight costs from source to destination are constant and that the supply and demand quantities are equal and strictly known, so the market for the product is well-balanced It thus involves a special type of linear integer programming, which becomes stochastic since the constraints or parameters are random variables from a known or unknown distribution Several studies have formulated well-known deterministic models under probabilistic restrictions The transformed models mostly keep the confidence level at a given minimum constant or else minimize the error level Also, there is a multi-choice stochastic transportation problem, which introduces several unit costs In this study, we try to simulate Roys (2014) multi-choice stochastic transport model with random supply and demand quantities from a given Weibull distribution and compare the results of distribution and total costs.
Keywords: Simulation-Based Optimization; Multi-Choice Transportation Problem; Weibull Distribution.
A Bibliometric and content analysis of core researches on Operations Research
by Nikunja Mohan Modak, Alok Raj, Shib Sana
Abstract: Operations research emerged as a discipline after the Second World War due to its efficiency to make better decisions. The socio-economic environment world over has changed over the last 50 years, and operations research has played a vital role in complex decision-making. Advanced analytical methods of operations research are effective for improving decision-making and solving real life problems in business, industry, and society. This work presents a bibliometric and content analysis of core publications of operations research. This study exclusively considers publications with operations research in their title. We have used SCOPUS, a reliable database, to collect relevant data. It presents overall publications and structure of citations. It comprehensively studies the significant contributions of journals, universities, and countries on this topic. Using the visualisation of similarities viewer software, it presents network visualisations and analyses co-occurrence of keywords, co-citations, and bibliographic coupling of institutions and countries. Finally, the paper reviews highly influential articles in this field and provides comprehensive direction for future research in this field.
Keywords: operations research; Scopus; bibliometrics; VOS viewer.
Cash discount associated permissible delay sensitive Economic Order Quantity (EOQ) systems in favor of deteriorating commodity of Weibull distribution receptive demand and Shortages
by Rakesh Tripathi, Hari Shyam Pandey
Abstract: Inflation is a major problem in the entire world. Inflation and time value of currency both are analogous to each other. In this learning demand is measured two Weibull parameters. Shortages and deterioration both are taken into account. Main goal of this work is to find the most favorable replenishment planning so that total cost in minimized. Mathematical models are consequential under the four dissimilar circumstances. Optimal solution is obtained by solution procedure. Numerical designs are offered to confirm the model expected in study. Sensitivity analysis is specified for distinction of dissimilar parameters. Mathematics 7.0 is used for finding numerical outcomes.
Keywords: Cash- discount; trade credits; Weibull distribution; deterioration; cycle time; Demand.
An Optimization and Simulation Hybrid Approach for Maternal Healthcare Facility Location-Allocation in the Indian Context
by Ankit Chouksey, A.K. Agrawal, Ajinkya N. Tanksale
Abstract: This paper addresses availability and accessibility issues related to maternal healthcare facilities, basic healthcare to neonatal services, prevailing in India. These issues are resolved on economic considerations in establishing and running healthcare facilities and the expenses on travel incurred by mothers-to-be (MTBs) in visiting them. This problem is formulated as a mixed-integer linear programming model for determining optimal number and locations of the healthcare facilities within the specified geographical boundary. The formulation also decides optimal allocation of MTBs to these centers that are available within a reasonable distance. Service demand of MTBs was considered to be deterministic. In reality, the demand, being random, may be high to cause healthcare service quality negatively. Hybrid optimization and simulation approach shows that the service quality deteriorates with the increase in demand variability or its mean value. This information is useful in deciding additional number of the service facilities to provide satisfactory service quality.
Keywords: Optimization and Simulation; Maternal healthcare; Facility location; Mixed-integer programming; Monte-Carlo simulation.
A Comparison of Different Mathematical Models for the Job Sequencing and Tool Switching Problem with non-identical Parallel Machines
by Dorothea Calmels
Abstract: This paper addresses the generalisation of the NP-hard job sequencing and tool switching problem with non-identical parallel machines and sequence-dependent setup times where a set of jobs is to be scheduled on unrelated parallel machines with machine-dependent processing and tool switching times. Three different mathematical models for two different objectives are presented and applied to newly generated test instances. The instances are compared and analysed using a commercial solver and an iterated local search heuristic. Overall, it is shown that the solution quality obtained by the mathematical models depends on the size of the problem instance as well as the tool requirements. The precedence-based formulation is superior in general to the position-based and time-index-based formulation for dense problem instances while the position-based formulation works well for sparse problems. With an increasing problem size, the metaheuristic requires significantly less time to find near-optimal solutions than the mathematical models.
Keywords: mixed integer programming; job sequencing and tool switching; tooling constraints; parallel machines; sequence-dependent setup times.
Replenishment Decision for Ameliorating Inventory with Time Dependent Demand and Partial Backlogging Rate
by Vijay Vir Singh, Yusuf Ibrahim Gwanda
Abstract: In contrast to deterioration, amelioration refers to a situation where stocked items incur increased value, quantity, or utility while in stock. It is generally seen in poultry, piggeries, wine industries, etc. when these items are kept on the farm or the sales counter, they usually incur increase in quantity and value. In this research, we study an inventory model that outlines the optimal replenishment decision for ameliorating items with a partially backlogged time-varying demand rate to raise productivity and understand opportunity cost due to lost sales. Until recently, most of the research in inventory has been focused essentially on deteriorating inventory, giving little or no attention to its ameliorative nature. Therefore, in this research, we developed an EOQ model for such items with time-dependent demand and partial backlogging rate. Using the differential calculus concept, the various Inventory optimizing functions, like total cost, number of replenishments, backlogging factors, etc. are computed.
Keywords: Ameliorating Inventory; Time dependent demand; Replenishment decision; Partial backlogging; Lost sales.
A multi-objective optimization model for production and transportation planning in a marine shrimp farming supply chain network
by Mr.Chaimongkol Limpianchob, Masahiro Sasabe, Shoji Kasahara
Abstract: The traditional operation of marine shrimp farming is widely practiced in Southeast Asia. Giant freshwater prawn farming is one of the main types of farming that also still operates traditionally. Many of these farms operate without advanced techniques for production planning, inventory control, and transportation strategic decisions throughout the supply chain network which are among the most important managerial activities in commercial farming. Maintaining product freshness is of vital importance for aquaculture product Therefore, this paper develops a multi-objective mixed-integer linear programming model for a marine shrimp farming supply chain network design problem. The problem is to plan production and control inventory according to constraints while maximize total profit surplus and minimize shortest route. A multi-echelon, multi-facility, and multi-period mathematical model is proposed such that real conditions are considered. In the end, some numerical illustrations are provided to show the proper Pareto solutions considering all of the objectives
Keywords: supply chain network; mixed-integer linear programming; marine shrimp farming; giant freshwater prawns; multi-objective optimization; production planning and inventory control; transportation planning.
Time Paradox in Transportation Problems
by Sonia, Subramanian Chidambaran
Abstract: The boom in E-commerce industry in recent years is reshaping delivery landscape and logistics needs to keep pace with this rapid e-commerce growth. One of the most important customer satisfaction KPIs in the e-commerce industry is to reduce the delivery time and is a key focus area for the market players.The present paper attempts to address this challenge by introducing the concept of a paradox in a time minimizing transportation problem (TMTP) which is a concave minimization problem. The paradoxical situations in cost minimizing transportation problems, fixed charge transportation problems, minimum cost flow problems and in many other variants of transportation problem have been extensively researched and well applied except for TMTPs. After defining the time paradox in a TMTP, we develop the condition of its existence. Further, a comparative study of paradox for a cost minimizing and time minimizing transportation problems has also been carried out.
Keywords: Time paradox; Time Minimizing Transportation Problem; Bottleneck Transportation Problem.
A Hybrid Tournament Differential Evolution Algorithm for Solving Optimization Problems and Applications
by Md Akhtar, Amalesh Kumar Manna, Avijit Duary, Asoke Kumar Bhunia
Abstract: The goal of this work is to propose a hybrid algorithm, combining Differential Evolution algorithm and Tournamenting process, for solving constrained and bound-constrained optimization problems. Considering different options of binary tournamenting, six variants of the proposed hybrid algorithm are developed. To test the efficiency and performance of the proposed hybrid algorithm, twelve benchmark optimization problems are considered and solved. From the obtained results of these benchmark problems, proposed six different variants of the hybrid algorithm are compared numerically as well as graphically. From these comparisons, the best variants of the hybrid algorithm for solving constrained and bound-constrained optimization problems are identified separately. Then by using these best variants, three well known engineering design problems are considered and solved. The computational results are compared with the results of some of the existing algorithms available in the literature. In each case, it is observed that the proposed algorithm performs well.
Keywords: Global Optimization; Constrained Optimization; Bound-constrained Optimization;Differential Evolution; Tournamenting.
A partial back-ordering inventory model for log-gamma degenerating items with quadratic demand and shortages
by K. Senbagam, M. Kokilamani
Abstract: The intention of this article is to explore; an inventory system for degenerated items includes quadratic demand and log-gamma degeneration rate. Degeneration is permitted on inventory which follows two-parameter log-gamma distributions. Stock out is permitted in the inventory system is completely and partially back-ordered. The cost of holding cost is assumed to be a constant. The aim of this economic order quantity pattern is to minimize the entire revenue of inventory optimization. The model can be used for businesses where the demand and rate of deterioration depend on time. Here we provide some numerical examples and sensitivity analysis are supported to demonstrate the solving methodology.
Keywords: The inventory system; degenerated items; Log-gamma degeneration; quadratic function; partial back-ordering; shortages.
Modeling and simulation of Bernoulli feedback queue with general customers' impatience under variant vacation policy
by Amina Bouchentouf, Mouloud Cherfaoui, Mohamed Boualem
Abstract: This article deals with a feedback queueing system with variant of a multiple vacation policy, balking, reneging, retention of reneged customers. On arrival a customer activates an impatience timer which is generally distributed. If a customer's service has not been completed before the customer's timer expires, the customer leaves the system. It is supposed that the impatience timer depends on the states of the server. The general distribution of impatience times as well as server's states-dependent reneging makes the analysis of the considered model difficult. We establish the equilibrium analysis of the queueing model, we derive its performance measures and examine it by a series of simulation experiments through a discrete-event simulation (EDS). This approach is appropriate for modeling such complex environment, it attempts to replicate the behavior of the system providing the system characteristic estimations.
Keywords: queueing; vacation; balking; reneging; general customers' impatience; discrete-event simulation.
Hybrid Multi-Objective Evolutionary (H-MOE) Algorithm for Solving RALB-II Problem
by Vigneshwar Pesaru, S. Venkataramanaiah, Mukund Nilakantan Janardhanan
Abstract: In this paper, we propose a mixed integer programming (MIP) model with dual focus on minimization of cycle time and total assembly line cost simultaneously. Due to NP-hard nature of RALB (Rubinovitz and Bukchin 1991), and also to avoid local minima in search space, a hybrid multi-objective evolutionary (H-MOE) algorithm developed based on the features of Non-dominated sorting Genetic Algorithm (NSGA-II) along with Simulated annealing (SA) local search algorithm and is used to solve the RALB-II problem. We conducted number of experiments using data sets selected from published literature (Mukund et al 2017b) and evaluated the performance of the proposed hybrid multi-objective evolutionary algorithm. From the experimental results, it is found that the proposed hybrid algorithm outperformed the algorithm proposed by Mukund et al (2017b) in five out of seven cases on saving in cycle time and four out of seven in terms of total cost saving.
Keywords: Hybrid Algorithm; Multi-objective; NSGA; Robotic Assembly Line; Parameter tuning.
Application of Multi-Objective Probabilistic Fractional Programming Problem in Production Planning
by Berhanu Belay, Srikumar Acharya, Rajashree Mishra
Abstract: This paper presents the application of multi-objective probabilistic fractional programming problem in production planning. The production planning model for a manufacturing company that produces multi-products with a specified period is formulated by considering some of the parameters in the right hand side of the constraints as random variables following continuous distribution namely gamma distribution. The formulated mathematical model is a multi-objective probabilistic fractional programming problem. In the solution procedure, the deterministic equivalent of the probabilistic programming problem has not been obtained. The analytical method for multi-objective fractional programming problem has also not been applied to solve the proposed model. A stochastic simulation based genetic algorithm is applied to solve the proposed model directly. A set of Pareto optimal solutions is obtained for the formulated production planning problem
Keywords: genetic algorithm;multi objective programming problem;probabilistic programming problem; stochastic simulation; fractional programming;production planning;gamma distribution;Pareto optimal solution.
Application and Technique of Inventory Control Theory in Pharmaceutical Sciences.
by Atma Nand
Abstract: The pharmaceutical inventory product management poses a unique problem in the hospitals and pharmacy shops. The inventory control theory plays a significant part in a pharmacological business. For every business, the simple and intuitive interpretation of all the available stocks is important. The determination of the importance of a pharmaceutical drug item should also consider other considerations, such as how severely its unavailability would affect patients. All the drugs are associated with an expiry date, so it is necessary to keep minimal safety stocks of pharmaceuticals products. In this article, we have been suitably classified all available pharmaceutical models and discussed in detail. This article is an overall review of most of the available pharmaceuticals Inventory literature.
Keywords: medicinal inventory; pharmacological inventory; inventory; perishable products; RFID technology.
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.
A FEPQ model of sustainable items with time and stock dependent demand under trade credit policy
by Bijoy Krishna Debnath, Pinki Majumder, Uttam Kumar Bera
Abstract: Now-a-days Sustainable Fuzzy Economic Production Quantity (s-FEPQ) models gets more
highlighted over classical Fuzzy Economic Production Quantity (EPQ) models. In this paper,
we developed a fuzzy inventory model of sustainable items under time dependent quadratic
rate of fuzzy demand and exponential holding cost where shortages are allowed and are fully
backlogged considering obsolescence cost and carbon emission cost. Also the developed model is compared with stock dependent fuzzy demand. The proposed fuzzy inventory model is solved
via Generalized Hukuhara derivative approach. Two different cases are considered by using
Generalized Hukuhara-(i) differentiability and Generalized Hukuhara-(ii) differentiability. For the first time, in this sustainable fuzzy EPQ model, an alternative approach of payment is proposed. After that, the proposed model has been solved by using multi-objective genetic
algorithm. The proposed model and technique are lastly illustrated by providing numerical
examples. Results from two methods are compared and some sensitivity analyses both in
tabular and graphical forms are presented and discussed.
Keywords: Sustainable EPQ model; fully backlogged shortages; carbon emission; trade credit; alternative approach of payment.
Self-Adaptive Bee Colony Optimisation Algorithm for the Flexible Job Shop Scheduling Problem
by Malek Alzaqebah, Salwani Abdullah, Rami Malkawi, Sana Jawarneh
Abstract: The bee colony Optimisation (BCO) algorithm is a nature-inspired algorithm that models the natural behaviour of honey bees as they find nectar and share food sources information with other bees in the hive. This paper presents the BCO algorithm for the flexible job-shop scheduling problem (FJSP), in addition, to improve the neighbourhood search in the BCO algorithm we introduce a self-adaptive mechanism to the BCO algorithm (self-adaptive-BCO algorithm) for adaptively selecting the neighbourhood structure to enhance the local intensification capability of the algorithm and to help the algorithm to escape from a local optimum. We carry out extensive computational experiments on three well-known benchmarks for flexible job-shop scheduling. The BCO algorithm is compared with the self-adaptive-BCO algorithm to test the performance of the latter. The results demonstrate that the self-adaptive-BCO algorithm outperforms the BCO algorithm, the proposed approach also outperforms the best-known algorithms in some datasets and it is comparable with these algorithms in other datasets.
Keywords: bee colony Optimisation; flexible job shop; adaptive neighbourhood search strategy.
Solving industrial multiprocessor task scheduling problems using an improved monkey search algorithm
by MARIAPPAN KADARKARAINADAR MARICHELVAM, MARIAPPAN GEETHA
Abstract: This paper addresses multiprocessor task scheduling in a multistage hybrid flow shop environment which has been proved to be strongly NP-hard. An improved monkey search algorithm (IMSA) is proposed to solve this problem. The objective is to minimize the makespan which is the completion time of all the tasks in the last stage. The proposed algorithm is tested with three types of problems. A real industrial data is first used. Then, random problem instances are generated and finally, the benchmark problems addressed in literature are also considered. In all the three cases, the results are compared with earlier reported algorithms in the literature and the computational results reveal that the proposed algorithm is competent.
Keywords: scheduling; hybrid flow shop; multiprocessor tasks scheduling; NP-hard; monkey search algorithm; makespan.
A Developed Multicriteria Group Decision Making Method Based on
Interval Valued Hesitant Fuzzy Linguistic Term Sets and Mentality Parameter
by Nesrin Halouani
Abstract: Hesitant Fuzzy Linguistic Term Set (HFLTS) can be considered as a very practical tool in addressing decision problems where people are hesitant to provide their linguistic assessments while avoiding the possible loss of information. Therefore, HFLTS enhances the flexibility to get and represent linguistic information.
This paper deals with this kind of decision making problems by proposing the concept of Interval Valued Hesitant Fuzzy Linguistic Term Sets (IVHFLTS) since it can be considered as an extension of both a linguistic term set and an interval-valued hesitant fuzzy set. This new combination deals with both quantitative and qualitative evaluations. By introducing the mentality parameter for IVHFLTS, we develop a multicriteria group decision making model to deal with hesitant fuzzy linguistic information which avoids the possible loss of information. In order to show the applicability and the efficiency of the proposed method, an example for the selection of the best alternative is given as well as the ranking of the alternatives from the best to worst. The promising numerical results prove that this model is available.
Keywords: Multicriteria Group Decision Making; Hesitant Fuzzy Linguistic Term Sets; mentality parameter; valued interval.