International Journal of Operational Research (132 papers in press)

Regular Issues

• 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.
  DOI: 10.1504/IJOR.2020.10030065
   
  
• Two-Commodity Multi-Server Queueing-Inventory System with Compliment Product and Classical Retrial Facility  
  by Jeganathan Kathirvel, Nithya M, C. SUGAPRIYA, Selvakumar Subramanian 
  Abstract: This article explores a two-commodity stochastic queueing-inventory system (TCSQIS) along with a multi-server and a classical retrial facility. In this two-commodity, the first one is called a primary product (PP) and the second one is called a complimentary product (CP). The TCSQIS provides a multi-server service facility to an arriving customer in order to reduce the loss of arrival and increase more sales and profit. Suppose the arriving customer sees that all the servers are engaged or there are no sufficient products in the system, they go to an infinite orbit compulsorily. The customers in orbit can approach the system under classical retrial policy, whenever they confirm that the system must consist of at least one server that is free and positive stock. The stationary probability vector is obtained by the matix-geometric approach (MGA). Further, adequate examples are provided to explore the proposed model.
  Keywords: multi-server; compliment product; infinite orbit; classical retrial policy; waiting time.
  DOI: 10.1504/IJOR.2021.10042182
   
  
• Advanced Payment Strategy for EOQ Model regarding Perishable Product With Maximum Lifetime,Customer Return, Preservation Technology and Partial backlogging  
  by Ravendra Kumar, Ravish Kumar Yadav 
  Abstract: Many products, like fruits, vegetables, etc. have a certain life that depends on the preserving conditions and also the demand of these products depends on their life. Selling price and the investment in a preservation mechanism are the most important factors in inventory management. An economic order quantity model is proposed focusing on perishable products having a certain lifetime. A preservation mechanism has been implemented to extend the products lifetime. Two different models with or without shortages are presented here. Concavity of objective function is established using several theorems. Developed model is validated with the help of numerical examples. Sensitivity is also carried out. The results show that the retailers profit reduces when dealing with products for which customers are more concerned about the products lifetime. The findings also suggest that in order to increase profit, retailers should design an inventory policy that controls purchase costs and interest.
  Keywords: advanced payment; EOQ; perishable product; customer return; preservation technology; partial backlogging.
  DOI: 10.1504/IJOR.2021.10042219
   
  
• Multi-Objective Faculty Course Timeslot Assignment Problem for Tutorial and Laboratory Courses  
  by Sunil Bhoi, Jayesh Dhodiya 
  Abstract: This study applied a multi-objective zero-one integer programming model to resolve the university timetabling problem of assigning timeslots to faculty member for tutorial and laboratory courses. The model helps prepare an efficient and effective timetable by optimising the satisfaction levels of faculty members, administrators, and students. It ensures that no conflict occurs between tutorial and laboratory courses and their proper distribution across days and sessions. Furthermore, the model assigns all batches of the same course to one faculty member to save preparation/laboratory setup time. Appropriate scheduling allows each batch to utilise their time for self-study courses, library, and extracurricular activities. The model systematically incorporates two-hour laboratory courses in two consecutive break-free timeslots and the proper utilisation of the library and preparation for self-study courses by students. The model was applied to a technical institute using hypothetical data, and effectual and feasible schedules were generated using fuzzy programming. The solutions were obtained using LINGO 18.0 software.
  Keywords: timetabling; university course scheduling; multi-objective programming; zero-one linear programming; fuzzy programming technique.
  DOI: 10.1504/IJOR.2021.10046716
   
  
• Pascal’s Triangle Graded Mean Defuzzification Approach For Solving Fuzzy Assignment Models by Using Pentagonal Fuzzy Numbers  
  by Zeina Mohammed  
  Abstract: The fuzzy assignment models (FAMs) have been explored by various literature to access classical values, which are more precise in our real-life accomplishment. The novelty of this paper contributed positively to a unique application of pentagonal fuzzy numbers for the evaluation of FAMs. The new method namely Pascal’s triangle graded mean (PT-GM) has presented a new algorithm in accessing the critical path to solve the assignment problems (AP) based on the fuzzy objective function of minimising total cost. The results obtained have been compared to the existing methods such as, the centroid formula (CF) and centroid formula integration (CFI). It has been demonstrated that operational efficiency of this conducted method is exquisitely developing an optimal solution (Opt. Sol.) depending on the corresponding path by the new tender algorithm.
  Keywords: centroid formula; centroid formula integration; CFI; fuzzy assignment models; FAMs; optimal solution; Opt. Sol.
  DOI: 10.1504/IJOR.2021.10042897
   
  
• Pricing and Ordering Strategy for New Product and Buyback Strategy for Used Product from Retailer’s Point  
  by DHARMESH KATARIYA, Kunal Shukla 
  Abstract: Today environmental spectrums are much considered while purchasing a new product because of global awareness about sustainability of environment, hence an interest for use of restored products has increased. The retailer is a decision-maker; retailer sells a new product to the consumers and also collects the used sold products for reselling. In this deteriorating inventory model, the demand rate of new products is a nonlinear function and demand rate of used buyback products is linear function of selling price and time-dependent respectively. Shortages are allowed and the unsatisfied demand is partially backlogged. The objective is to maximise total profit per time unit for a retailer concerning to optimise selling price, order quantity for a new product, and quantity of used buyback products simultaneously. Global optimality is verified by Hessian matrix method and graphically. This model is explained through a numerical example, sensitivity analysis, and managerial insights. Ultimately, some concluding remarks with future scopes are discussed.
  Keywords: inventory; used buyback product; price dependent demand; deterioration; partial backlogging.
  DOI: 10.1504/IJOR.2021.10042898
   
  
• An Adaptive Large Neighborhood Search Algorithm for Blocking Flowshop Scheduling Problem with Sequence-Dependent Setup Times  
  by Faezeh Bagheri, Morteza Kazemi, Ardavan Asef-Vaziri, Mahsa Mahdavisharif 
  Abstract: Flowshop scheduling problem (FSP) belongs to the classical combinatorial optimisation problem and takes different forms under different production conditions. To make the general form of FSP closer to the real production environment, two assumptions, including blocking and sequence-dependent setup time, were added. The first attempt of the current research work is proposing a mathematical model according to two different viewpoints about blocking occurrence affected by sequence-dependent setup time that try to use the dead time (blocking or idle time) for setting-up the next job. Due to the complex intrinsic of combinatorial problems, achieving the exact result on a large-scale through a mathematical model is almost complicated. The second attempt is developing an adaptive large neighbourhood search algorithm to solve the problem on a large-scale which is accelerated by a new constructive heuristic algorithm. Extensive computational experiments on various size problems support the efficiency of the proposed algorithms.
  Keywords: flowshop; blocking; sequence-dependent setup time; heuristics algorithm; adaptive large neighbourhood search algorithm; mathematical modelling.
  DOI: 10.1504/IJOR.2021.10042973
   
  
• Impact of Waste Management and Regulatory Mechanism on Sustainable EOQ Model with Controllable Non-Instantaneous Deterioration and Trade Credit-and Carbon-Sensitive Demand  
  by Sharad Kumar, Seema Agarwal, Dharmendra Yadav 
  Abstract: Non-instantaneous deterioration, trade credit, waste management, carbon sensitive demand, and preservation technology all have a significant impact on inventory control policies. So, taking into cognisance of these issues, an economic order quantity model has been developed to minimise the total inventory cost by considering the preservation technology (PT) to mitigate the rate of deterioration and to increase the length of the non-deterioration period. The objective of achieving financial sustainability is accomplished by taking into account the trade credit period. Consumers are becoming more environmentally conscious. As a result, it is assumed that demand rate is a function of the trade credit period and carbon level. Shortages are allowed with partially backlogging at a constant rate. To reduce carbon emissions, two different mechanisms, namely carbon tax and carbon cap-and-trade, are used. A solution algorithm is developed to obtain an optimal solution. The findings revealed that total inventory costs are reduced efficiently due to preservation technology and trade credit. According to the findings, environmentally friendly decision-makers preferred a carbon tax mechanism over a carbon cap-and-trade system. Analysis also suggested that the trade credit period has a positive impact on the total inventory cost.
  Keywords: waste management; trade credit; carbon tax; carbon cap-and-trade; partial backlogging.
  DOI: 10.1504/IJOR.2021.10043154
   
  
• Optimization, Planning and Mutualization of Chicken Production in a Multi-Supplier, Multi-Period, and Multi-Horizon Poultry Network: Case Study  
  by Tahraoui Nacéra, Lamia Triqui-Sari, Mohammed BENNEKROUF 
  Abstract: This study addresses the planning issue related to the broilers production in a poultry network. where the major problem is the imbalance between the supply and the demand. To this fact, the produced quantity is unstable, which influences selling prices. To effectively solve this problem, we have developed a generic linear programming model in order to facilitate the decision-making in production. The formulation is motivated by a real application case of the poultry chain in the city of Tlemcen. In this context, several configurations were tested: an economic configuration determined by financial planning, and a mutual configuration promoting the service quality. Also, we have developed a mathematical model for an integrated multi-horizon approach to organise the production rotation of all breeding farms over the long-term. Promising results have increased the breeder’s profit, and the service quality where all actors, breeders as well as consumers, will be in a win-win situation.
  Keywords: production planning; broilers chicken; linear programming; cooperative planning; integrated multi-horizon optimisation; case study.
  DOI: 10.1504/IJOR.2021.10043657
   
  
• Analysis on multi service interruption in the Queuing system  
  by S. Maragathasundari, P. Manikandan 
  Abstract: This article is about a queuing system in which the server is interrupted twice and then the repair process is carried out immediately without delay. After the service ends, if there is no client in the system, the server can choose to take a Bernoulli vacation. In addition, consequent occurrence of interruption of the system is an inevitable feature in our daily life activities. Also, during the repair process of second type interruption, process of Reneging happens. The queuing issue is drawn closer through supplementary variable technique of queuing hypothesis and the comparing framework likelihood producing capacity of the line length and the various queue measures are determined. Also, the queuing model is well explained by means of real-life application and processed by the way of numerical structure and graphic representation. This model means making a supervisor aware of the structural difficulties of a client-server-based framework and recognising the basic rules of investigation.
  Keywords: web server; reneging; service interruption; repair process.
  DOI: 10.1504/IJOR.2021.10043989
   
  
• Expectation and Fractile Models for Decentralized Distribution Systems under Demand Uncertainty and their Computational Methods  
  by Ichiro Nishizaki, Tomohiro Hayashida, Shinya Sekizaki, Naomichi Tani 
  Abstract: In this study, we deal with the expectation and the fractile models for obtaining a Nash equilibrium point of the two-stage game for describing the competition and cooperation in decentralised distribution systems with stochastic demands, and develop computational methods. In the first stage of the equilibrium problem, each retailer independently determines the inventory level, and in the second stage for the coordination of retailers, the addition profit arising from the transshipment of the leftover inventories of all the retailers is maximised. Formulating the transshipment of the leftover inventories as a two-stage programming problem with simple recourse, we define an allocation rule based on the optimal dual solution of the transshipment problem which belongs to the core of the cooperative game. Using numerical examples, we demonstrate the effectiveness of the expectation and the fractile models, and examine the validity of their computational methods.
  Keywords: decentralised distribution systems; equilibrium points; expectation and fractile models; two-stage games; computational methods.
  DOI: 10.1504/IJOR.2021.10044002
   
  
• Arbitrage Opportunity Estimation: The Case of the Cobb-Douglas Production Function  
  by Sergey Anokhin, Maxim Bushuev, Elena Akerman, Vladislav Spitsin, Dmitry Anokhin 
  Abstract: The entrepreneurship literature has recently become aware of the phenomenal promise of efficiency evaluation techniques for gauging one of its key concepts arbitrage opportunities. Unfortunately, the use of DEA, the dominant efficiency evaluation approach, for this purpose is limited by some of the properties of the method. In this paper we develop an alternative method that could be used to assess opportunities for imitation (arbitrage) available to entrepreneurial firms. We adapt the minimum performance inefficiency technique to the Cobb-Douglas production function, compare the new method to the dominant efficiency estimation techniques that could be used to measure arbitrage opportunity, and run a Monte-Carlo experiment to explore its applicability to alternative types of production functions typically tackled with data envelopment analysis. We show that the new method may provide more accurate results than the mainstream approaches, and demonstrate a real-life application of the technique in the publishing industry setting.
  Keywords: data envelopment analysis; DEA; minimum performance inefficiency; MPI; entrepreneurship; arbitrage opportunities; Cobb-Douglas.
  DOI: 10.1504/IJOR.2021.10044099
   
  
• An Advanced Fuzzy Approach for Assessing Supply Chain Resilience in Developing Economies  
  by Saleh Fahed Alkhatib  
  Abstract: This paper aims to develop an advanced fuzzy approach to assess the supply chain resilience (SCRE) in the developing economies under high uncertainty. The new approach incorporates the fuzzy decision-making trial and evaluation laboratory (FDEMATEL), the modified fuzzy best worst method (FBWM), and fuzzy techniques to order preferences by similarity to ideal solution (FTOPSIS) in a novel way to employ their merits. A systematic literature review of the SCRE dimensions was conducted to identify main SCRE factors. The FDEMATEL method obtained the relationships among these dimensions to provide a better understanding of this system. The FBWM weights these dimensions and identify their relative importance, and finally, the SCRE level was evaluated by the FTOPSIS method. A real case of the leading non-financial companies in the Middle East and North Africa (MENA) region were investigated. The research results revel several findings that provide useful insight for the MENA companies and academia.
  Keywords: supply chain resilience; SCRE; MENA region; fuzzy set theory; FDEMATEL; fuzzy best worst method; FBWM; FTOPSIS.
  DOI: 10.1504/IJOR.2021.10044173
   
  
• Examination and Class Timetabling Problem: A Case Study of an Indian University  
  by A.K. Agrawal, Susheel Yadav, Amit Ambar Gupta, Shubhendu Pandey 
  Abstract: Timetabling problem is basically an optimisation problem and is a subset of scheduling problems. A timetabling problem is a problem where events (classes, examination and student) have to be ordered in time slots while satisfying some basic constraints. Here the basic aim is to satisfy various constraints, such as, no student can be scheduled for more than one course at the same time and the maximum number of students that can be scheduled to any particular class must not exceed the sitting capacity of the class. So the timetabling problem is basically arranging the given set of values (class, subject and students) in such a manner such that the resulting schedule should fulfil all the above stated hard constraints and try to give more close values near to the soft constraints (such as proper distribution of classes over available slots). In the present work, examination timetable and class timetable problems have been considered with several practical features. The problems are formulated as mathematical models and heuristic approach is also proposed to solve them. The heuristic uses the basic framework of genetic algorithm.
  Keywords: timetabling; soft constraints; heuristics; genetic algorithm; mathematical modelling.
  DOI: 10.1504/IJOR.2021.10044175
   
  
• On Modeling and Lot Sizing of the Newsvendor Problem with Surrogate Product  
  by Layek Abdel-Malek, Pinyuan Shan, Roberto Montanari 
  Abstract: There is a growing interest in the newsvendor problem and its extensions. One of these extensions is in the area of product substitution. In this work, we model the situation where two perishable products are considered, a primary product and a surrogate one. The primary product yields higher profit than the surrogate. The objective of the model is to find the optimal lot sizes of both products that minimise the total ordering cost (alternatively, maximise the profit). Numerical analysis and examples to show the contribution of the surrogate approach to the overall performance of the policy are presented as well as some managerial insights. The applications of this model can occur in retail of perishable commodities, fashion sector, and products of high rates of technological obsolescence as well as service industries such as hotel reservation and car rentals.
  Keywords: newsvendor problem; stochastic optimisation; supply chain management; product substitute; decision making.
  DOI: 10.1504/IJOR.2021.10044176
   
  
• Pythagorean Fuzzy Goal Programming: A Novel Approach and its Application  
  by Sayan Deb, Sahidul Islam 
  Abstract: The present study discusses a novel goal programming technique for solving multi-objective problems. The technique is named Pythagorean fuzzy goal programming. The improved score function of the Pythagorean fuzzy sets has been used to develop the technique. Goal programming (GP) techniques have been proven to be very useful in the field of operations research. GP techniques are used to find the best compromise solutions for a multi-objective programming problem. An example of a supplier selection model is provided where the traditional intuitionistic fuzzy GP technique fails to optimise but the proposed technique provides an optimal solution. This technique is useful for the systems where the other GP techniques fail to provide any results.
  Keywords: Pythagorean fuzzy goal programming; goal programming; Pythagorean fuzzy sets; supplier selection model.
  DOI: 10.1504/IJOR.2021.10044247
   
  
• A Fuzzy Logic Approach for Housing Affordability Level Analysis  
  by Nerda Zura Zaibidi, Adyda Ibrahim, Nor Syuhaddah Saiddin 
  Abstract: The issue of house prices are either too high or unaffordable is widely discussed. The price index indicates residential units’ prices continue to climb from one year to another. Consequently, the number of unsold residential units becomes high. This issue has raised concern among researchers to analyse about the affordability level among house buyers. This study aims to measure the affordability level of house buyers in Malaysia by using a fuzzy logic approach. Two inputs are being considered; house price ranges and household incomes while for the output is the affordability level. The findings show that the affordability level for the middle-income earners in Malaysia is low. This finding can help the authorised body to look carefully into this issue and find a strategic solution to overcome the problems to avoid the housing development turning into a slump.
  Keywords: fuzzy logic; house prices; housing affordability; housing market.
  DOI: 10.1504/IJOR.2021.10044471
   
  
• Addressing Stock Market Time Series Trends and Volatility Using Optimized DE-LSTM Model  
  by Raghavendra Kumar, Pardeep Kumar, Yugal Kumar 
  Abstract: Accurate time series prediction is a most challenging trend before research communities in the machine learning era. Stock market data is the most dynamic and volatile time series data that holds the world economy. Recent studies proposed various core and hybrid machine learning models to get accurate stock forecasting. Existing work put forward long short-term memory (LSTM) to implement sequential time series data. In this paper, a new nonlinear hybrid model is proposed using customised LSTM and differential evolution (DE) algorithms. DE brings the optimisation of selection of parameters and provides stability between complexity and learning performance of the hybrid model. The paper explores the forecasting accuracy of the stock market trends and volatility using hybrid model DE-LSTM. The proposed hybrid model obtained significant improvement as MAE, RMSE and MAPE are 0.21167, 2.48198 and 2.68331 respectively, practiced for a diversified portfolio of Bombay Stock Exchange, India (BSE30).
  Keywords: stock market trends; volatility; time series data; long short-term memory; LSTM; differential evolution; DE.
  DOI: 10.1504/IJOR.2021.10044472
   
  
• A tabu search heuristic for the outsourcing risk management problem in multi-echelon supply chains  
  by Arian Nahangi, Mohamed Awwad 
  Abstract: Globalisation has made supply chains more vulnerable to risk factors, increasing the associated costs of outsourcing goods. This paper considers a multi-echelon supply chain model with global and domestic raw material suppliers, manufacturing plants, warehouses, and markets. The problem is formulated as a mixed-integer linear programming model. Due to the NP-hard nature of the problem, a tabu search heuristic is proposed to solve small, medium, and large problem instances. A quadratic multi-regression analysis is used to analyse the performance of the proposed tabu search heuristic. The statistical analysis shows that increasing the number of iterations and neighbours will increase run time and reduce total supply chain cost. The proposed tabu search heuristic proved to provide an efficient solution, including large problem instances, in a timely manner.
  Keywords: multi-echelon supply chain; risk management; outsourcing; tabu search; multi-regression.
  DOI: 10.1504/IJOR.2021.10044473
   
  
• Integrated Bioethanol-Gasoline Supply Chains (IBGSCs) created in response to government policy changes in Nebraska  
  by Davoud Ghahremanlou, Wieslaw Kubiak 
  Abstract: The US Government applied its policy-making power to mitigate the negative impact of COVID-19 on integrated bioethanol-gasoline supply chains (IBGSCs). In order to study the IBGSCs which evolve in response to change of policies, Ghahremanlou and Kubiak (2021) developed an algorithm, referred to as extended lean model (ELM). In this paper, we apply the ELM to real-life data for the State of Nebraska and solve 21,420 alternative policy scenarios to optimality to investigate and compare IBGSCs created due to the changes in policies. The case study shows that minimum tariffs for blending US and non-US bioethanol with gasoline needs to be 0.531 and 0.35 $ gal respectively to permit the government to shift bioethanol production in order to meet demand for ethanol used for producing sanitisers to avert COVID-19 from spreading. Finally, we provide a number of policy recommendations and directions for further research.
  Keywords: COVID-19; oil war; supply chain management; operations management; stochastic programming; location allocation; government policies; sustainability.
  DOI: 10.1504/IJOR.2021.10044574
   
  
• Operational Research Application to Minimize the Uncertainty of Time and Cost in the Main Domains of Industrial Engineering by Using Quality Engineering Techniques  
  by Ramin Rostamkhani, Mohammad Hossein Karimi Gavareshki, Morteza Abbasi, Mahdi Karbasian 
  Abstract: The present research attempts to introduce an operational research model to minimise the uncertainty of time and cost in the main domains of industrial engineering (IE) by using quality engineering techniques (QET). The research approach is based on three processes. The first process reviews QET among the most important IEF. The second process relates to the influential factors. The third process concentrates on the operational research model by applying the fuzzy multi-objective linear programming. A numerical application for the third process is explained. The assessment results are endorsed by the management representatives’ views in the organisation. The first innovative aspect of the research is the combination of QET and IE in the operational research model to save time and cost within the organisation. The second novelty of the current study is the use of quantum mechanics perspectives for the time concept in the long-term.
  Keywords: uncertainty; industrial engineering; quality engineering techniques; QET; multi-objective linear programming.
  DOI: 10.1504/IJOR.2021.10044581
   
  
• An Efficient Merge Search Matheuristic for Maximising Net Present Value in Project Scheduling  
  by Dhananjay Thiruvady, Su Nguyen, Christian Blum, Andreas Ernst 
  Abstract: Resource constrained project scheduling (RCPS) is an important combinatorial optimisation problem with many practical applications. With complex requirements such as precedence constraints, limited resources, and finance-based objectives, finding optimal solutions for large problem instances is very challenging. To address this challenge, we propose a new matheuristic algorithm based on merge search and parallel computing to solve RCPS with the aim of maximising the net present value. This paper presents merge search, a novel matheuristic designed for RCPS, which is a variable partitioning and merging mechanism to formulate restricted integer programs. Parallel ant colony optimisation can generate excellent solutions for RCPS, and we use this method to generate the solution pool. The experimental results show that the proposed method outperforms the current state-of-the-art algorithms on known benchmark problem instances. Further analyses also demonstrate that the proposed algorithm is substantially more efficient compared to its counterparts in respect to its convergence properties.
  Keywords: project scheduling; net present value; NPV; merge search; construct; merge; solve and adapt; CMSA; parallel ant colony optimisation.
  DOI: 10.1504/IJOR.2021.10044652
   
  
• Efficient mixed-integer linear programming formulations for the satellite broadcast scheduling problem  
  by Ananthu Krishna S, GIRISH B. S, Devendra Prakash Ghate 
  Abstract: Satellite broadcast scheduling problem (SBSP) is an important optimisation problem in satellite communication systems and is known to be NP-complete. The existing state-of-the-art solution methodologies for the SBSP include neural networks, evolutionary algorithms, and other heuristic approaches. This paper proposes two new mixed-integer linear programming (MILP) formulations for the problem. The performance of the proposed MILP formulations has been evaluated using benchmark problem instances and several other test instances on a commercial MILP solver. Computational results show that the proposed formulations outperform the existing approaches in terms of solution accuracy and computation time.
  Keywords: satellite communications; satellite broadcast scheduling; NP-complete problem; mixed-integer linear programming.
  DOI: 10.1504/IJOR.2021.10044724
   
  
• Technical note: A closed form solution to the k-centra location problem  
  by Trevor Hale, Crystal Welker, Ryan Pepper, Faizul Huq 
  Abstract: This research delineates a new heuristic that solves the k-centra location problem on a simple distribution network. This treatise differs from most location research in that we employ a novel, graph theoretic based k-centra approach that seeks to find the location that minimises the sum of the distances to the k furthest existing facilities. The k-centra problem generalises the classic minimax and minisum location problems: If k = 2, the problem reduces to the centre (minimax) location problem, whereas if k = n, the problem reduces to the median (minisum) location problem. This research has direct application to the location of a distribution centre on a simple origin-to-destination distribution network to improve service levels.
  Keywords: facility location; k-centra location; distribution network.
  DOI: 10.1504/IJOR.2021.10044729
   
  
• An Improved Clustering Heuristic in Cellular Manufacturing Systems  
  by Arindam Majumder, Dipak Laha 
  Abstract: This paper presents a construction heuristic in cellular manufacturing systems with the objective of maximising grouping efficacy. The proposed method is derived from three criteria, namely, Pearson correlation coefficient, the concentration of operations for a particular machine cell pair or a part family pair, and the density of operations for a particular machine-part cell comprising a machine cell and a part family. The proposed method consists of two phases. In the first phase, the machine-groups and part-families are constructed using Pearson correlation coefficient clustering technique. The second phase involves in selecting the machine-groups and part-families to build the respective machine-part cells to cluster machines and parts into the corresponding machine-groups and part families for the cellular manufacturing problem. The exhaustive computational results based on a set of different benchmark problem instances demonstrate that the proposed method is relatively superior to some well-known state-of-the-art methods.
  Keywords: cellular manufacturing; group technology; grouping efficacy; construction heuristic; correlation coefficient; concentration of operations; density of operations; optimisation.
  DOI: 10.1504/IJOR.2021.10044762
   
  
• Improving Passenger Satisfaction at Kuwait International Airport by using Multi Objective Optimization  
  by Ahmad Al-Sultan, Ahmad Alsaber 
  Abstract: Efforts to optimise the quality of services that passengers experience during the check-in process at major airport terminals are important discriminators in deciding the overall quality of the airports themselves. Improvement of passenger services during check-in involves reduction in queues and waiting times. This however, may require additional expansion projects to be undertaken by the authority. Nevertheless, such expansion projects involve conflicting ideas from multiple interest groups due to the absence of any existing efficient simulation model. This paper presents a multi-dimensional approach towards finding an optimal and feasible solution to the resource allocation problem at check-in stages in airports. The proposed methods employ optimisation techniques such as mixed integer goal programming (MIGP) and genetic algorithm (GA) to offer a comparative multi-solution outcome towards optimising the passenger quality of services at airport departure check-in. By adopting the proposed approaches, one can expect to achieve the following objectives: 1) finding an optimum allocation for airlines to different check-in zone area; 2) obtain a feasible planning to undertake decisions concerning check-in area expansion projects reaching their desired goals to improve passenger level of service under limited budget and area space.
  Keywords: airport terminal; integer programming; simulation; mixed integer goal programming; MIGP; Kuwait.
  DOI: 10.1504/IJOR.2021.10044942
   
  
• A Multi-Item EOQ Model for Deterioration Items with Ramp-Type Demand and Partial Backlogging under Carbon Emission and Inflationary Environment  
  by Vipin Kumar, Anupama Sharma, C.B. Gupta 
  Abstract: Carbon emissions contribute most in the global warming. To minimise the adverse effect of global warming on society, countries are focussing to control CO2 and industries are investing in green products. All of this, inclined the regulatory authorities and customers towards the environment. Thus, present study considers a novel multi-item inventory model for green products. Here, demand rate is considered as a function of initial inventory level, time, and carbon emission. This study also incorporates the carbon emissions associated with different attributes of inventory management such as procurement, purchasing, and holding inventory. Shortages are allowed and depend on the waiting time. The retailer adopts the supplier’s trade credit strategy and study is carried out the inflationary environment. This model aims to optimise the total inventory cost subject to the upper cap of carbon emission. Observation based on analysis indicates some empirical observation. The sensitivity analysis concerning critical parameters showed that the optimal policy for green products is highly sensitive to the costs of shortages, and backlogging. Results indicate that incorporation of carbon issue while modelling for the green products is worthwhile for environment. Around 38% changed is observed in the inventory cost due to the change in scale parameter of demand.
  Keywords: green products; trade credit; partial backlogging; multivariate ramp-type demand; deterioration.
  DOI: 10.1504/IJOR.2021.10045067
   
  
• Multi-Start Constructive Heuristic Through Descriptive Statistical Metrics: The Dhouib-Matrix-4 (DM4) Metaheuristic  
  by Souhail Dhouib 
  Abstract: In this paper, we design and develop a new metaheuristic named Dhouib-Matrix-4 (DM4). This method is based on a multi-start structure, where in each start a diversification phase is ensured by a constructive heuristic entitled Dhouib-Matrix-TSP1 (DM-TSP1) and an intensification phase is guaranteed by a novel local search method named far-to-near (FtN). Several descriptive statistical metrics (range, mode, standard deviation, etc.) are used in the heuristic DM-TSP1 in order to explore different realisable solutions. Respectively, these realisable solutions are exploited as starting points by the FtN method using several perturbation techniques (insertion, exchange and 2opt). The performance of the proposed method DM4 is tested on the travelling salesman problem using the well-known TSP-LIB benchmark instances with integer and real distances. Experimental results demonstrate that our approach DM4 is very competitive compared to the last developed metaheuristics.
  Keywords: Operational research; Combinatorial optimization; Travelling Salesman Problem; Optimization; Metaheuristic; Heuristic; Dhouib-Matrix; Computer Science; Artificial Intelligence.
  DOI: 10.1504/IJOR.2021.10045069
   
  
• Optimal Ordering Policies for Stock-dependent Demand under Partial Linked-to-Order Credit Period  
  by Nita Shah, Ekta Patel, Kavita Rabari, Hardik Soni 
  Abstract: Trade credit is recognised as an important strategy to increase productivity in inventory management. Hence, an economic order quantity model for stock-dependent demand is proposed under linked-to-order fully and partial delay in payment by considering the practical scenario. The items gradually deteriorate. More precisely, the payment scheme is structured as follows: the order quantity is greater than or equal to a predetermined quantity then full trade credit is offered else partial trade is offered. Shortages are not allowed. The algorithmic procedure to find the optimal replenishment cycle is developed. Finally, managerial insights are drawn to observe the applicability of the proposed model and perform sensitivity analysis on different inventory parameters.
  Keywords: inventory model; trade credit; partial trade credit; linked-to-order trade credit; stock-dependent demand; deterioration.
  DOI: 10.1504/IJOR.2022.10045390
   
  
• Cooperative Advertising Coordination in Two-Level Supply Channel using Differential Game  
  by Peter Ezimadu, Jonathan Tsetimi 
  Abstract: There are a lot of models in the cooperative advertising literature, however, only a few have considered channel coordination, and much fewer on the possibility of implementation. This work examines cooperative advertising in a bilateral monopoly using game theory. It considers a two-way coordination strategy in which the manufacturer who is the Stackelberg leader engages in national advertising and provides subsidy for retail advertising, while the retailer who is the follower engages in local advertising and provides motivational advertising support to the manufacturer. The work considers four game scenarios. For each scenario, it determines the players’ optimal advertising efforts and payoffs based on the advertising supports. The work observes that the players’ performances and channel performance are worst with non-provision of support. The retailer performs best with manufacturer’s support, while the manufacturer and the entire channel performances are best with mutual support for each other.
  Keywords: bilateral monopoly; channel coordination; cooperative advertising; co-op advertising; differential game; local advertising; national advertising; Stackelberg game; supply chain; supply channel.
  DOI: 10.1504/IJOR.2022.10045561
   
  
• An optimal integrated inventory management for vendor buyer with substitutable deteriorating product under joint replenishment and cost of substitution  
  by Ranu Singh, Vinod Kumar Mishra 
  Abstract: Today’s supply chain network needs a new paradigm shift in the way the buyer and the vendor collaborate. The present article develops an integrated approach for vendor-buyer considering substitutable deteriorating products under joint replenishment. In this study, we consider two mutually substitutable products, and substitution is only taken for the buyer end. In case of one product is out of inventory, the demand for that product can be partially met by the stock of the other product otherwise demand is lost. In the case of substitution, an extra cost of substitution gets involved. The demand, deterioration, and production rate are assumed to be deterministic and constant. The objective of this study is to find the optimal number of deliveries, lot size, production quantity, and stock levels to minimise the integrated entire cost incurred by vendor and buyer. The solution method is suggested to derive the optimal value. A numerical study is provided to show the applicability of the model. Sensitivity analysis and managerial implications are presented to illustrate the validity of the integrated model. The comparative analysis shows that in case of product substitution the effective integrated cost is considerably reduced.
  Keywords: vendor-buyer; optimal policy; deterioration; substitutable product; integrated model; joint replenishment.
  DOI: 10.1504/IJOR.2022.10045692
   
  
• Metaheuristics for Time-Dependent Vehicle Routing Problem with Time Windows  
  by Yun-Chia Liang, Vanny Minanda, Aldy Gunawan, Hsiang-Ling Chen 
  Abstract: Vehicle routing problem (VRP), a combinatorial problem, deals with the vehicle’s capacity visiting a particular set of nodes while its variants attempt to fit real-world scenarios. Our study aims to minimise total travelling time, total distance, and the number of vehicles under time-dependent and time windows constraints (TDVRPTW). The harmony search algorithm (HSA) focuses on the harmony memory and pitch adjustment mechanism for new solution construction. Several local search operators and a roulette wheel for the performance improvement were verified via 56 Solomon’s VRP instances by adding a speed matrix. The performance comparison with a genetic algorithm (GA) was completed with the same number of parameters and ran in the same computer specification to justify its performance. The results show that HSA can outperform the GA in some instances. The research outcomes suggest that HSA can solve TDVRPTW with comparable results to other commonly used metaheuristic approaches.
  Keywords: vehicle routing problem; VRP; time window; harmony search algorithm; HSA; genetic algorithm; metaheuristic.
  DOI: 10.1504/IJOR.2022.10045858
   
  
• Marketing Mix Optimisation with Practical Constraints  
  by Hsin-Chan Huang, Jiefeng Xu, Alvin Lim 
  Abstract: In this paper, we address a variant of the marketing mix optimisation (MMO) problem which is commonly encountered in many industries, e.g., retail and consumer packaged goods (CPGs) industries. This problem requires the spend for each marketing activity, if adjusted, be changed by a non-negligible degree (minimum change) and also the total number of activities with spend change be limited (maximum number of changes). With these two additional practical requirements, the original resource allocation problem is formulated as a mixed integer nonlinear program (MINLP). Given the size of a realistic problem in the industrial setting, the state-of-the-art integer programming solvers may not be able to solve the problem to optimality in a straightforward way within a reasonable amount of time. Hence, we propose a systematic reformulation to ease the computational burden. Computational tests show significant improvements in the solution process.
  Keywords: marketing mix optimisation; MMO; optimisation; semi-continuous variable; cardinality constraint; perspective reformulation; mixed integer quadratic program; MIQP.
  DOI: 10.1504/IJOR.2021.10045955
   
  
• ABC classification using MCDA, DDF and TOPSIS approach
  by Subhadip Sarkar 
  Abstract: This paper outlines the way of classifying stocking items using multi-criteria decision analysis into A, B and C classes. A combination of directional distance function and technique for order of preference by similarity to ideal solution are suggested in this regard to select the pair of ideal points. The prescribed model argues in favour of designing two frontiers to aid in the categorisation of items with an underlying assumption of convexity. Moreover, the proposed approach has a guaranteed solution in the presence of negative data where the conventional data envelopment analysis models find difficulties in measuring the convex efficiency scores for the given set of alternatives. The two frontier approach is meant for enclosing each alternative inside the worst and best frontiers. Unlike the traditional data envelopment analysis models two convex efficiency scores are computed. inefficiency scores deduced from these two frontiers are then used for measuring a proximity score which ultimately discriminates the items into three categories.
  Keywords: directional distance model; TOPSIS; multi-criteria decision making; MCDM.
  
  
• Modelling and simulation of multi-compartment vehicle routing problems to transport different types of solid waste
  by Yousra Bouleft, Ahmed Elhilali Alaoui 
  Abstract: : The waste management problem is an important sign of development in every city in the world. In this work, we introduce a new scheme that divides the entire waste management system into three levels: 1) transfer separated solid waste from different sources to the compartmentalised transfer station, each compartment accommodating one or more supplies of the same type of waste; 2) transport the separated solid waste from the transfer station to the treatment plants, each plant belonging to a specific specialty via a compartmentalised fleet; 3) transfer the waste produced from the treatment plants to the nearest landfill. In this context, we present mathematical formulations of the waste management system using linear programs. Since the problem is NP-hard, we adapt a genetic algorithm to solve the second level. Numerical experiments show that after adapting our proposed approach, we get high-quality solutions to collect and transport a large quantity every day.
  Keywords: multi-compartment vehicle routing problems; solid waste management system; separated solid waste transport; genetic algorithm.
  
  
• A simple use of OR techniques to complete seismic records without magnitude value  
  by M.C.M. Rodrigues, C.S. Oliveira 
  Abstract: We are working on modelling the phenomenon of seismic occurrences in the Azores region which requires, among other characteristics, the knowledge of the magnitude of past events. Approximately 18,000 seismic records are available, but more than 3,000 (17%) have no information on magnitude. In this paper, we propose a methodology to generate the missing values of magnitude. This is based on an unusual but very simple application of OR, where pseudo-random values of magnitude are generated according to the statistical distribution of magnitude in records where information is complete. Special care is exercised on the upper tail of the distribution. The procedure does not determine the magnitude of an earthquake at a specific date, but the records become more complete in the sense that the generated values have a statistical distribution identical to the observed values. Statistical tests and the confirmation of the Gutenberg-Richter law validate the methodology.
  Keywords: natural phenomena simulation; simulation to complete missing values; upper tail generation; OR application; magnitude estimation; seismic process of occurrences; earthquake hazards; Azores.
  DOI: 10.1504/IJOR.2022.10047772
   
  
• The impact of project network topology and resource restrictions on the performance of schedule generation schemes: a comparative study
  by Raafat Elshaer 
  Abstract: Parallel, and serial schedule generation schemes (SGS) are the core of most heuristic solution procedures for the resource-constrained project scheduling problem. The solution-quality of the generated schedules using the two schemes depends on the projects network topology and resource restrictions. According to the most recent publication, the network topology is measured using four indicators: serial/parallel, activity distribution, length of arcs, and topological float, whereas resource restrictions are assessed using two parameters: resource use and resource-constrainedness. The main objective is to investigate the impact of the projects network topology and resource parameters on the performance of the two schemes. A study-based generated datasets has been applied using a genetic algorithm with the two schemes. The computational results demonstrate that out of four indicators, serial/parallel has the most significant impact on distinguishing between the performance of the two SGS schemes. Moreover, resource use also impacts the discrimination between the two schemes
  Keywords: project network topology; resource-constrained project; schedule generation schemes; SGS.
  
  
• Optimal testing resource allocation for software system: an approach combining interval-valued intuitionistic fuzzy sets and AHP
  by Rubina Mittal, Rajat Arora, Anu Gupta Aggarwal 
  Abstract: Among all the phases of the software development process, testing consumes the majority of the total available resources. Therefore, it is vital to efficiently manage the testing resources for the development of quality software. In this study, we propose an approach for allocating the resources optimally among the modules of the software system considering the quality characteristics discussed in the literature. The methodology incorporates interval-valued intuitionistic fuzzy analytic hierarchy process (IVIF-AHP) in the optimisation problem to maximise the reliability of the software subject to the availability of resources. Optimisation is grounded on Weibull testing effort-dependent failure phenomenon considering change point and logistic fault reduction factor. The weights obtained for modules through IVIF-AHP are further used in prioritising them in the optimisation problem. The detailed methodology has been discussed followed by a numerical illustration.
  Keywords: interval-valued intuitionistic fuzzy sets; analytic hierarchy process; AHP; resource allocation; software reliability growth model; SRGM; testing effort; optimisation.
  
  
• A Conditional Value-at-Risk (CVaR) approach to studying the Sustainable Crude Oil Supply Chains (SCOSCs) evolved due to change in government policies  
  by Davoud Ghahremanlou, Wieslaw Kubiak 
  Abstract: Recently US oil and bioethanol industries have faced drastic economic damage due to the 2020 Saudi Arabia-Russia oil price war and coronavirus disease (COVID-19), resulting in many bankruptcies. Government policies have brought these two main industries together to ensure sustainable crude oil supply chains, to combat global warming and energy insecurity. This motivated us to extend the study of Ghahremanlou and Kubiak (2021a) to protect the current and new SCOSCs against financial risks during economic crises by providing insights for the government and the investors, working to rescue the industries. We employ conditional value-at-risk, and develop a two-stage stochastic programming model. We perform a case study of the State of Nebraska by carrying out a computational experiment with 10,710 different policy scenarios. We recommend robust strategic investment decisions to businesses during policy changes within economic crises. We also identify resilient strategic investment decisions.
  Keywords: COVID-19; conditional value-at-risk; CVaR; government policies; economic crises; sustainable crude oil supply chain; two-stage stochastic programming.
  DOI: 10.1504/IJOR.2022.10046294
   
  
• Optimal Testing Resource Allocation for Software System: An approach combining Interval Valued Intuitionistic Fuzzy Sets and AHP  
  by RUBINA MITTAL, RAJAT ARORA, Anu G. Aggarwal 
  Abstract: Among all the phases of the software development process, testing consumes the majority of the total available resources. Therefore, it is vital to efficiently manage the testing resources for the development of quality software. In this study, we propose an approach for allocating the resources optimally among the modules of the software system considering the quality characteristics discussed in the literature. The methodology incorporates interval-valued intuitionistic fuzzy analytic hierarchy process (IVIF-AHP) in the optimisation problem to maximise the reliability of the software subject to the availability of resources. Optimisation is grounded on Weibull testing effort-dependent failure phenomenon considering change point and logistic fault reduction factor. The weights obtained for modules through IVIF-AHP are further used in prioritising them in the optimisation problem. The detailed methodology has been discussed followed by a numerical illustration.
  Keywords: interval-valued intuitionistic fuzzy sets; analytic hierarchy process; AHP; resource allocation; software reliability growth model; SRGM; testing effort; optimisation.
  DOI: 10.1504/IJOR.2022.10046600
   
  
• The impact of project network topology and resource restrictions on the performance of schedule generation schemes: A comparative study  
  by Raafat Elshaer 
  Abstract: Parallel, and serial schedule generation schemes (SGS) are the core of most heuristic solution procedures for the resource-constrained project scheduling problem. The solution-quality of the generated schedules using the two schemes depends on the project’s network topology and resource restrictions. According to the most recent publication, the network topology is measured using four indicators: serial/parallel, activity distribution, length of arcs, and topological float, whereas resource restrictions are assessed using two parameters: resource use and resource-constrainedness. The main objective is to investigate the impact of the project’s network topology and resource parameters on the performance of the two schemes. A study-based generated datasets has been applied using a genetic algorithm with the two schemes. The computational results demonstrate that out of four indicators, serial/parallel has the most significant impact on distinguishing between the performance of the two SGS schemes. Moreover, resource use also impacts the discrimination between the two schemes.
  Keywords: project network topology; resource-constrained project; schedule generation schemes; SGS.
  DOI: 10.1504/IJOR.2022.10046619
   
  
• A Fuzzy Economic Order Quantity Model for Multiple Stage Supply Chain with Fully Backlogged Shortages Derived without Derivatives under the Effect of Human Learning  
  by Richi Singh, Ashok Kumar, Dharmendra Yadav 
  Abstract: In industries, inventory managers face major difficulties in inventory planning when the available information fluctuates abruptly or is unclear. This ambiguity can be treated appropriately by using fuzzy sets. Moreover, human learning is effective in reducing the level of fuzziness over the infinite horizon. In the present study, a fuzzy three-stage (buyer-distributor-vendor) EOQ model is developed. In this model, all the cost parameters are taken as fuzzy parameters. Shortages are allowed but fully backlogged at the buyer end. The novelty of the paper lies in deriving the fuzzy model by using the arithmetic-geometric inequality method and proposing four theorems based on optimal frequency for vendor and distributor, along with incorporating the concept of learning in fuzziness. Some numerical examples are taken to demonstrate the model in a better way. Also, a comparison among the results of this paper, and other papers are done with the help of an example, which shows that the present model better represents the practical financial situations. At last, sensitivity analysis concerning all parameters and managerial insights are presented to justify the significance of the model.
  Keywords: supply chain; arithmetic geometric inequality; fuzzy costs; learning; backlogging.
  DOI: 10.1504/IJOR.2022.10046637
   
  
• Comparative Study of Maximization Assignment Model by Existing Method and Newly Proposed Methods  
  by Agnivesh Tiwari, Kabir Chaudhary, Rahul Boadh, Yogendra Rajoria 
  Abstract: One of the simplest uses of linear programming is known as the assignment problem, which is a special case of the transportation problem. The assignment problem manages the inquiry about to dole out n-items to m-different items in the most ideal way for production planning, telecommunication, VLSI design, economics, etc. Many researchers developed newly proposed methods for solving assignment problems and others modified the Hungarian method. Therefore, in the present study, an effort has been made to solve the real-life balance and unbalance type profit maximisation assignment problem used by ten newly proposed methods such as MAP, MSEI, ATOC, NAZs and six others methods, and compared results with Hungarian method. This study found that Method 8 takes the least time for computation of both type problems as compared with other methods. This paper advocates that new researchers and scientists may use the newly proposed Method 8 in place of the existing method.
  Keywords: assignment problem; Hungarian method; optimal solution; profit maximisation.
  DOI: 10.1504/IJOR.2022.10046644
   
  
• On the number of occupied resources in a system with resource sharing between service-oriented customers  
  by Toky RAVALIMINOARIMALALASON, Mirisoa RAKOTOMALALA, Falimanana RANDIMBINDRAINIBE 
  Abstract: We present analytical and generalised expressions of the numbers of occupied resources in a system providing several services to its customers. This system can be assimilated to a multi-service queue. We considered that the required numbers of resources for each service are deterministic, then random variables number of resources is studied in second part. Analysis carried out on the resource occupations have made possible the dimensioning of the total number of necessary resources that must be deployed on the queue server. They are to serve the customers up to a certain quality of service in terms of the probability of congestion. Reported to a single-service case, we found from the analytical expressions established in this work the probability of congestion in the Fry-Molina traffic model. The results are illustrated with numerical case studies.
  Keywords: queueing; dimensioning; multi-service; resource occupation; resource sharing.
  DOI: 10.1504/IJOR.2022.10046726
   
  
• A NEW GENERALIZED MEDIAN BASED ESTIMATOR OF THE FINITE POPULATION MEAN  
  by Dharmendra Yadav, Dinesh K. Sharma, S.K. Yadav 
  Abstract: To enhance the performance of an estimator, the use of additional information on study variables instead of the auxiliary variables may be a good alternative in survey sampling as it does not increase the survey cost. One of the examples of such additional information is the use of the median of the main variable. As there is no need for full information on units of the population under consideration for the median, so many times it is known to us. In the present article, we have developed an extended ratio estimator for the population mean utilising a given population median of the study variable. We have driven out the expressions for Bias of suggested estimator along with its MSE up to the approximation of degree one. The optimal value of the characterising scalar has also been derived using the method of maxima and minima. The conditions under which the suggested estimator is more efficient than previous estimators are also obtained. Our findings, in theory, are supported by the numerical illustration consisting of three different natural populations. The efficiency of the suggested estimator over competing estimators is also presented in the form of graphical representation.
  Keywords: median; ratio estimator; bias; MSE; efficiency.
  DOI: 10.1504/IJOR.2021.10046753
   
  
• International Journal of Operational Research: A Retrospective Overview between 2005 and 2020  
  by Santosh Baheti 
  Abstract: The study presents a retrospective analysis of the International Journal of Operational Research (IJOR) across its 16 years of publication, 2005 to 2020. IJOR is a journal of international repute that publishes original and peer-reviewed research in the management sciences, decision sciences, and operation research domain. The journal reached its 17th year of publishing in 2021. This study provides a comprehensive overview of 1,023 publications using the bibliometric data analysis technique. The study focuses on the contribution of IJOR to the knowledge domain through publishing trends, authorship patterns, dominant authors, prominent articles, nature of studies, and thematic analysis. Co-occurrence analysis of all keywords, co-authorship, citation and co-citation analysis of authors, countries, and institutions is performed through VOS Viewer software. The findings of the study emphasise the relationship of IJOR to different fields.
  Keywords: bibliometrics; Scopus; operational research; VOS Viewer.
  DOI: 10.1504/IJOR.2021.10047331
   
  
• Solution of a mining equipment maintenance system model in imprecise environment  
  by Ashok Kumar Shaw, Mostafijur Rahman, Sankar Prasad Mondal, Banashree Chatterjee, Shariful Alam 
  Abstract: In this paper, a mining equipment maintenance system is depicted by a mathematical model described in terms of a pair of differential equations. The impreciseness involved in a real-life situation is accounted in this paper letting the initial condition and associated parameters to be imprecise in nature. Two most popular mathematical knowledge dealing with the sense of uncertainty, namely the fuzzy and interval environments are utilised here to analyse and solve the proposed model under uncertainty. The solutions of the uncertain differential equations (both interval and fuzzy) corresponding to the model are getting inspired by the generalised Hukuhara derivative of a set valued function. Different crispification techniques of converting the fuzzy and interval solutions into their crisp representatives are manifested to compute and compare the score of feasible decisions under uncertainty.
  Keywords: mining equipment maintenance problem; generalised Hukuhara derivative; fuzzy differential equation; FDE; interval differential equation; removal area method; mean of centre method.
  DOI: 10.1504/IJOR.2022.10047628
   
  
• MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns  
  by Kasun Bandara, Rob Hyndman, Christoph Bergmeir 
  Abstract: The decomposition of time series into components is an important task that helps to understand time series and can enable better forecasting. Nowadays, with high sampling rates leading to high-frequency data (such as daily, hourly, or minutely data), many datasets contain time series data that can exhibit multiple seasonal patterns. Although several methods have been proposed to decompose time series better under these circumstances, they are often computationally inefficient or inaccurate. We propose a procedure to decompose time series with multiple seasonal patterns that is suited to a wide range of high-frequency data. The procedure for multiple seasonal trend decomposition (MSTL) introduced in this paper extends the traditional seasonal-trend decomposition using Loess (STL) algorithm, allowing the decomposition of time series with multiple seasonal patterns. In our evaluation on synthetic and a perturbed real-world time series dataset, compared to other decomposition benchmarks, MSTL demonstrates competitive results with lower computational cost. The implementation of MSTL is available in the R package forecast.
  Keywords: time series decomposition; multiple seasonality; MSTL; TBATS; STR.
  DOI: 10.1504/IJOR.2022.10048281
   
  
• Analysis of (MAP, PH)/(PH, PH)/1 Retrial Queueing Model with Standby Server, Collision of Orbital Customers, Breakdowns, Two-way communication, Phase Type Repairs, Constant Retrial Rate and Impatient Behaviour of Customers  
  by AYYAPPAN Govindan, Thilagavathy Karthikeyan 
  Abstract: Having modelled our system using standby server whenever the main server unavailable due to breakdowns and analysed the constant retrial policy for the orbital customers. The incoming arrival (IA) of customers follows the Markovian arrival process (MAP). The outgoing arrival (OA) of customers, service for both incoming and outgoing arrival of customers, repairs are all based on the phase-type (PH) distributions. Using Matrix analytic method, we investigate the steady state probability vector of the system. We described the busy period, cost analysis of the system and some characteristics of the system measures. We evaluate some numerical and graphical representation for the elucidation of our proposed model.
  Keywords: PH Distribution; MAP; Standby Server; Impatient Behaviour; Constant retrial rate; Two-way Communication.
  DOI: 10.1504/IJOR.2022.10048364
   
  
• Performance Analysis of Reverse Osmosis System using Reliability Availability Maintainability and Dependability (RAMD) Technique  
  by Anas Maihulla, Ibrahim Yusuf 
  Abstract: Reverse osmosis systems have recently made significant contributions to the growth of renewable filtered water sources. RAMD (reliability, availability, maintainability, and dependability) is an engineering tool for addressing operational and safety risks in systems. It seeks to identify a system's weakest areas in order to improve overall system dependability. Elaborate RAMD analysis of these systems is presented starting from the subsystem level, then the overall system. Further, an improved reliability block diagram is presented to estimate the RAMD performance of six practical reverse osmosis system. A novel approach is also presented in order to estimate the best probability subsystem. The monitoring of the critical subassemblies of reverse osmosis system will increase the possibility not only for improving the availability of the system, but also to optimise the mean time to the system failure (MTTF). It will also inform the operators about the status of the various subsystems of the system.
  Keywords: reverse osmosis; reliability; availability; safety; maintainability; subsystems; dependability.
  DOI: 10.1504/IJOR.2021.10049213
   
  
• A Novel Technique for Solving Bi-Level Linear Fractional Programming Problems with Fuzzy Interval Coefficients  
  by Nejmaddin Sulaiman, Gulnar Wasim, Basiya Kakawla 
  Abstract: In this paper, a bi-level linear fractional programming problem (BILLFPP) with fuzzy interval coefficient (FIC) is contemplate where really all of it is coefficients in the goal function and constraints are fuzzy intervals (FIs). Firstly, to resolve this issue, we are going to construct two LFPP with fuzzy coefficients. Before all else, of these issues is a LFPP where all of coefficients are upper approximations of FIs and the other is a LFPP where all of coefficients are lower approximations of FIs. Secondly, the BILLFPP is transformed to the form of single goal LFPP and QFPP. We address problems with a factorised or non-factorised optimisation problem and homogeneous or non-homogeneous constraints. Our proposed technique is based on a mathematical model that converts the QFPP to a LPP by solving the problem in an algebraic expression with a Taylor series. This technique, which is based on the LPP solution, can be applied to specific problems. NLFPP containing nonlinear constraints, on iterative processes, it decreases the overall processing time. To explain, the novel technique for solving BILLFPP by taking numerical examples and compare with Jayalakshmi (2015) and Syaripuddin et al. (2017).
  Keywords: LFPP; bi-level linear fractional programming problem; BILLFPP; FBILLFPP; BILLFPP with fuzzy interval; FBILLFPP with FIC; QFPP; Taylor series; a novel technique.
  DOI: 10.1504/IJOR.2022.10049559
   
  
• An integrated Systematic Layout Planning (SLP), Analytical Hierarchy Process (AHP) and Non-Linear Programming (NLP) approach to facility layout design  
  by Vikas Singla 
  Abstract: This study has three aims: 1) finding alternative layout designs through procedural approach of SLP of a large-scale auto component manufacturer; 2) examine optimality of identified alternatives by using both qualitative and quantitative criteria and ranking them by using AHP method by collecting data from decision-makers of similar 132 manufacturers; 3) identifying most optimal design by using mathematical optimisation model of NLP. Results of SLP provided three prominent quantitative criteria by comparing key performance indicators of four alternative designs with those of existing layout. Informal discussions extracted major qualitative criteria. Rating of all six criteria indicated distance and cost of change being primary influencers. Results of NLP were able to identify one most optimal alternative from feasible four derived from SLP. The study derives its originality by dealing with shortcoming of SLP approach of over emphasis on subjective criteria and of metaheuristic methods of assigning excessive importance to metaheuristic methods.
  Keywords: facility layout; systematic layout planning; SLP; analytic hierarchy process; AHP; nonlinear programming; NLP; quadratic assignment problem; QAP.
  DOI: 10.1504/IJOR.2022.10049593
   
  
• PROPOSAL OF A NUMERICAL APPROXIMATION THEORY TO SOLVE THE ROBUST CONVEX PROBLEM OF PRODUCTION PLANNING  
  by Marcelo Gonçalves, Rafael Wollmann, Raimundo Sampaio 
  Abstract: his research seeks to solve the production planning problem modelled as a queuing system to propose to managers a production planning model that uses efficient, simple and robust methods. First, a robust mathematical model of nonlinear programming was proposed considering the concepts of queuing systems to estimate production capacity. Next, this model was approximated by a family of affine functions using the strategy of approximating a convex set by a polyhedral set. Finally, a theorem was proposed to demonstrate that a robust nonlinear programming model can be approximated by a robust linear programming model. From a numerical experiment with data from an electronic equipment company, it was possible to observe the effectiveness of the approximation method.
  Keywords: robust optimisation; linear programming; convex programming; queuing systems.
  DOI: 10.1504/IJOR.2022.10049618
   
  
• Optimal Location Prediction for Emergency Stations using Machine Learning  
  by Prasham Sheth, Praxal Patel, Priyank Thakkar 
  Abstract: Time is a critical aspect in emergency circumstances like medical crises, natural disasters, breaking out of a fire, etc. The average response time of emergency services is on the rise in recent times owing to the growing traffic. This has raised some serious concerns for people’s safety. It is easy to perceive that optimally located emergency stations (e.g., ambulance, fire station) can help in these situations by minimising travel time to reach the location of casualty. With this motivation, we propose an approach which employs K-medoids driven by extreme gradient boosting (XGBoost) for predicting optimal locations of emergency stations. The proposed approach is validated on real datasets, namely: New York City, USA 100-metre Grid Coordinates, NYC Taxi Trip Duration, KNYC Metars 2016 and FDNY Firehouse Listing dataset and the results demonstrate that the proposed method reduces normal average response time and allows serving more locations.
  Keywords: emergency station; optimal location prediction; OLP; machine learning; XGBoost; K-medoids; average response time.
  DOI: 10.1504/IJOR.2022.10049826
   
  
• An EOQ Model for Deteriorating Item with Ramp type Linear Time Dependent Demand and Time Dependent Partial Backorder  
  by Prokash Mondal, Asim K. Das, Tapan Kumar Roy, Surajit Naskar 
  Abstract: In this present article we have developed an economic order quantity (EOQ) model over a finite time horizon for an item with a liner time dependent demand rate with constant rate of deterioration in consideration of shortages (SFI policy) in inventory under permissible delay in payments and partial backlogging. Studied witnessed that the demand always play a pivot role in the inventory model, due to COVID crisis there is a shift in the paradigm on the demand characteristics. This model studied the shifting demand rate after stock out period. Mathematical models are also developed under two distinct circumstances, i.e., case 1: the trade credit is before the stock out period and case 2: the trade credit scheduled after stock out period. The results are illustrated with numerically and graphically. The sensitivity analysis of key parameters of the optimal solution has also been conducted to study the effect of the parameter.
  Keywords: inventory; economic order quantity; EOQ; deterioration; delay in payment; trade credit; backlog dependent.
  DOI: 10.1504/IJOR.2022.10049855
   
  
• Linearization of Non-Linear Programs using the Essence of Calculus and Integer Programming  
  by Matthew Zilvar 
  Abstract: This paper contains an approach to solve nonlinear programming (NLP) problems using a linearisation approach based on theorems of calculus. The solution method relies upon dividing functions with finite domains into a series of domains and coefficients used to model linear and nonlinear functions within a mixed integer linear program (MILP). Nonlinear terms are solved for in the objective function and constraints while achieving global optimality at a specified resolution using the international system of units (SI). An efficient solution method is provided by creating a set of MILPs that represent the same problem with different complexities and using the solutions to achieve global optimality. Numerical results and a comparison are provided. From the results an argument in the P versus NP problem is formed.
  Keywords: linearisation; nonlinear programming; integer programming; P vs. NP; calculus; logarithmic programming; transportation problem; set forming; complexity theory; global optimality.
  DOI: 10.1504/IJOR.2022.10050354
   
  
• A Comparison of a novel single reference point Multi-Attribute Decision Making Method with EDAS method  
  by Sirine Boujelben, Mohamed Souissi 
  Abstract: The present work aims to introduce a new method called the evaluation based on deviation from median attribute values (EDMAV) to solve the multi-attribute decision problems. This method is based on the deviation of each alternative from to the reference median solution with respect to each criterion. However, the proposed method combines the results of two different models to get the global score of each alternative, namely weighted arithmetic mean (WAM) and weighted median model (WMed). A ranking of alternatives is performed based on the value of a joint generalised criteria computed according to the results of these models. The proposed method is applied on illustrative example in order to illustrate its applicability, usefulness, and effectiveness and it has been compared with the evaluation based on distance from average solution (EDAS) method.
  Keywords: multi-attribute decision; operational research; weighted median; average; single reference point.
  DOI: 10.1504/IJOR.2022.10050398
   
  
• A Double-Pivot Degenerate-Robust Simplex Algorithm for Linear Programming  
  by Yaguang Yang, Fabio Vitor 
  Abstract: A double pivot simplex algorithm that combines features of two recently published papers by these authors is proposed. The proposed algorithm is implemented in MATLAB. The MATLAB implementation is tested, along with a MATLAB implemention of Dantzig’s algorithm, for several test sets, including a set of cycling linear programming problems, Klee-Minty’s problems, randomly generated linear programs, and Netlib benchmark problems. The test results show that the proposed algorithm, with a careful implementation is: 1) degenerate-robust as expected; 2) more efficient than Dantzig’s algorithm for large size randomly generated linear programming problems, but less efficient for Netlib benchmark problems and small size randomly generated problems in terms of CPU time.
  Keywords: double pivots; degenerate-robust; simplex method; linear programming; Klee-Minty cube.
  DOI: 10.1504/IJOR.2022.10050447
   
  
• General finite approximation of noncooperative games played in staircase-function continuous spaces  
  by Vadim Romanuke 
  Abstract: A method of general finite approximation of N-person games played with staircase-function strategies is presented. A continuous staircase N-person game is approximated to a staircase N-dimensional-matrix game by sampling the player’s pure strategy value set. The method consists in irregularly sampling the player’s pure strategy value set, finding the best equilibria in
  Keywords: game theory; payoff functional; staircase-function strategy; multidimensional-matrix game; approximate equilibrium consistency; equilibrium stacking.
  DOI: 10.1504/IJOR.2022.10050526
   
  
• DEVELOPMENT AND VALIDATION OF A HYBRIDIZED ALGORITHM INVOLVING AHP AND MACHINE LEARNING FOR AUTOMOBILE VEHICLE SELECTION  
  by Sanjeev Kumar, Ashirbad Sarangi, Rakesh P. Badoni, R.P. Mohanty 
  Abstract: The problem of selecting an automobile has always been one of the most complex decisions to make, given a person’s social and economic life. It is often resolved either through a qualitative judgement of vehicles or through multiple criteria decision-making (MCDM) methods in an algorithmic way. However, the modern machine learning (ML) procedures have surfaced themselves as efficient techniques in the field of recommendation engines (REs) to predict the items that may be useful to the customers according to their preferences. In this paper, an attempt has been made to study the automobile vehicle selection (AVS) problem in an innovative manner by hybridising the analytic hierarchical process (AHP) with the collaborative filtering (CF) technique to construct a selector to recommend the customers precisely one pair of cars that would suit best to their preference. The proposed algorithm provides an efficient way to map the satisfaction level of the customers by eliminating the vagueness and complexity in the selection process. We have validated the algorithm using real-life datasets collected by administering an exploratory survey across geographies, including India.
  Keywords: multiple criteria decision-making; MCDM; analytic hierarchical process; AHP; automobile vehicle selection; AVS; collaborative filtering; CF; recommendation engine.
  DOI: 10.1504/IJOR.2022.10050616
   
  
• Modelling MX/G/1 queuing system with optional second service under disaster and repairs with multiple adapted vacation policy  
  by S. Jeyakumar, B. Logapriya 
  Abstract: In this article, the queuing system with disaster is considered in which every customer will receive the essential service and demanded customer alone will receive second optional service. When the system is affected by any of the disaster, the server initiates the repair period and operates under multiple adapted vacation (MAV) policy causing all waiting and served customer to leave the system. Using supplementary variable technique, we procure the queue size distribution with few measures of performance. Expected queue length, expected waiting times and certain special cases are discussed. In addition, the effect of parameters is studied with a numerical illustration.
  Keywords: supplementary variable technique; second optional service; disaster; multiple adapted vacation policy.
  DOI: 10.1504/IJOR.2022.10050810
   
  
• A systematic literature review to measure lean, green and agile in manufacturing organisations  
  by Fadwa Bouhannana, Akram Elkorchi 
  Abstract: Most manufacturing companies are mainly interested in strengthening competiveness by concentrating on competitive priorities. The majority of companies have started implementing lean, green and agile paradigms in order to become more efficient and highly productive. To achieve those objectives, researchers around the world have been increasingly interested in developing tools to control the process of implementing these three paradigms in organisations. In this context, various approaches have previously been proposed in the literature. Consequently, a systematic review of measurement methods, such as leanness, greenness and agility, in manufacturing organisations was performed for the purpose of defining some guidance for managers and practitioners who are interested in measuring these three concepts. Therefore, 121 methods have been selected and analysed based on a set of comparative dimensions. The main strengths and weaknesses of the selected approaches are mentioned. Some literature gaps are highlighted, and a number of directions are provided for future research.
  Keywords: manufacturing; leanness; greenness; agility; literature review; score.
  DOI: 10.1504/IJOR.2022.10050966
   
  
• A multi-objective portfolio selection problem with parameters as interval type fuzzy set.  
  by Jayanti Nath, Sanjoy Chhatri, Debasish Bhattacharya 
  Abstract: A multi-objective portfolio selection problem with fuzzy parameters is studied here based on the possibility concept of fuzzy set theory. Here, for a given degree of membership ? of the fuzzy parameters, the problem has been reduced to an equivalent crisp problem. This reduced problem is then solved by the min-max goal programming (GP) method in one step. This approach gives the decision maker the flexibility to choose the solution of the problem for an assigned degree of satisfaction ? and concomitant risk (1
  Keywords: portfolio optimisation; fuzzy multi-objective linear programming; capital growth; return; risk; liquidity; dividend; min-max GP.
  DOI: 10.1504/IJOR.2022.10051487
   
  
• Applying Mathematical Modeling to the Factor Analyses of Obtaining GASR Funds for Universities in Japan  
  by Masashi Miyagawa, Takuro Matsumoto, Atsushi Inoue, Tatsuo Oyama 
  Abstract: First, we briefly explain the historical trend of the Japanese competitive research funding system, focusing on the grants-in-aid for scientific research (GASR). We provide mathematical models, such as logistic curves and Zipf’s model, to explain the trend of budgets for research promotion funds and their allocation to Japanese universities and research institutions. Subsequently, we evaluate the performance of Japanese universities from the perspective of obtaining GASR funds using Gini coefficients. We then build multiple regression models to quantitatively investigate the factors that determine and affect the dependent variables, such as the number of accepted GASR projects and number of distributed funds of the GASF projects, in which independent variables including the number of undergraduate students, external funds, operating expenses grants, and operating expenses grants to all university-specific project expenses, or the ratio of external funds per faculty member, may also be considered as influential factors. We apply multivariate analysis techniques such as cluster analysis and principal component analysis to determine the key factors for obtaining GASR, to classify Japanese universities with respect to their recent scenario for obtaining GASR funds and reveal the determining factors underlying these results.
  Keywords: competitive research fund; scientific research fund; mathematical model; logistic curve; Zipf’s model; multiple regression model; cluster analysis; principal component analysis.
  DOI: 10.1504/IJOR.2022.10051636
   
  
• Priority Study on Commodity Market Operation and Performance for Indian Investors  
  by Sanat Rout, Sadananda Sahoo, Rabindra Kumar Mishra 
  Abstract: The present research explores the investors’ behavioural intention towards the commodity market in an emerging economy. Drawing cues from the extant literature, this research identifies and empirically prioritises the dimensions of investor intention regarding commodity trading. Based on the RIDIT analysis, the findings indicate that a lower degree of risk, geopolitical changes, and a higher rate of return are the most important dimensions based on the respondent perceptions. These findings offer newer insights on this under-explored domain to facilitate conceptual development and policy formulation. The portfolio managers, market regulators, and financial institutions can take cues from the study findings to redesign their strategies for attracting investors to commodity exchanges.
  Keywords: commodities; investor; behavioural intention; emerging economy; RIDIT.
  DOI: 10.1504/IJOR.2022.10051839
   
  
• On state dependent batch service queue with single and multiple vacation under Markovian arrival process  
  by Gagan Kumar Tamrakar, Anuradha Banerjee 
  Abstract: : An infinite buffer batch service vacation queue has been studied where service rate of the batch is dependent on the size of the batch and vacation rate is dependent on the queue size at vacation initiation epoch. The arrivals follow the Markovian arrival process (MAP). For service rule, general bulk service (GBS) rule is considered. The service time and vacation time both are considered to be generally distributed. Several joint distributions of interest are obtained using the bivariate vector generating function method and the supplementary variable technique (SVT). Numerical results are presented to show the behaviour of the system performance to validate the analytical results.
  Keywords: Markovian arrival process; infinite buffer; GBS rule; bivariate VGF; supplementary variable technique.
  DOI: 10.1504/IJOR.2022.10052122
   
  
• On Solving Game Problem using Octagonal Neutrosophic Fuzzy Number  
  by R. Narmada Devi, S. Sowmiya 
  Abstract: Game theory deals with competitive situation where there are two or more opposing parties with conflicting interests are involved. A competitive situation will be called a game. In this paper, a new approach for selecting a best strategy for increasing the shares for two companies using octagonal neutrosophic fuzzy number is proposed. Further, convert a octagonal neutrosophic fuzzy number to neutrosophic fuzzy number by using deneutrosophication and finally get the fuzzy number by using fuzzification method. The obtained matrix represents fuzzy game matrix. This matrix is solved using game theory to obtain the best strategy for these companies.
  Keywords: octagonal neutrosophic fuzzy number; ONFN; DTNON: de-neutrosophication of trueness; DINON: de-neutrosophication of indeterminacy; DFNON: de-neutrosophication of falsity.
  DOI: 10.1504/IJOR.2022.10056357
   
  
• Multi-Echelon Reverse Supply Chain Network Design using New Ant Colony Optimization Algorithms  
  by Mostafa Ashour, Raafat Elshaer 
  Abstract: Reverse logistics (RL) is becoming more important in the general area of the industry due to environmental and business factors. Planning and implementing a suitable RL network can lead to more benefits, customer satisfaction, and a nice social image for businesses. Since such network design challenges belong to the NP-hard problem class, three proposed ant colony algorithms that differ in the heuristic information, and artificial pheromone trail calculation rules were developed to solve a designed distribution-allocation problem in multi-stage RL network with a fixed transportation cost in distribution network as well as variable cost of the route. Five network characteristics with different sizes are designed, and thirty instances are randomly generated for each network characteristic to evaluate the performance of the three developed ant colony optimisation (ACO) algorithms. Computational analysis of the results reveals the high quality and validity of the developed ACO algorithms when compared with the exact results.
  Keywords: logistics network; forward/reverse supply chain; single-objective; ant colony optimisation; ACO.
  DOI: 10.1504/IJOR.2022.10052168
   
  
• A LINMAP Approach for Determining Subjective Attribute Weights for Neutrosophic Multi Attribute Decision Making Models  
  by S. Paulraj, G. Tamilarasi 
  Abstract: The linear programming technique for multidimensional analysis of preference (LINMAP) is one of the well-known methods involved to solve multi-attribute decision-making (MADM) problems. Many authors developed LINMAP method based on fuzzy and intuitionistic fuzzy environment. In this paper, we develop a new method called neutrosophic Linear programming technique for multidimensional analysis of preference (LINMAP), which combines the single valued neutrosophic sets with LINMAP method. This paper establish the conventional LINMAP method to a neutrosophic MADM framework using single valued trapezoidal neutrosophic numbers and we obtain the attributes weight and ideal solution. A practical example is provided to show that our method is very effective for solving MADM problems with single valued trapezoidal neutrosophic number information. Comparative analyses with existing method are also furnished to shows the advantage of our proposed method.
  Keywords: single valued trapezoidal neutrosophic number; LINMAP; consistency and inconsistency measures; multi-attribute decision making; MADM.
  DOI: 10.1504/IJOR.2022.10052436
   
  
• Parallelization of multiple traveling salesman problem without returning to the starting node  
  by Vadim Romanuke 
  Abstract: A method of heuristically solving the non-classic multiple travelling salesman problem is suggested, where a dramatic computational speedup is guaranteed. The salesmen covering the route must not return to the starting node in this problem. A specific genetic algorithm is the solver. To get the speedup, the nodes should be separable so that they could be divided into two or more groups. Every two adjacent groups are connected by a node called the isthmus. The respective subproblems are solved independently, in parallel, whereupon their subroutes are aggregated through the isthmuses. This shortens the aggregated route on average, although it may be slightly longer in specific cases. Such an accuracy loss is 1% to 2% in the worst case for a few hundred thousands to millions of nodes, but instead the saved computational time is counted in days, weeks, and months. The efficiency of such a parallelisation dramatically grows as more isthmuses as distinct node group separators are found. If two successive subroutes are covered by the same number of salesmen, the constraint of that every node can be visited only by one salesman is easily satisfied by correcting the subroutes at the isthmus.
  Keywords: multiple travelling salesman problem; route length; genetic algorithm; parallelisation; isthmus; node group separability.
  DOI: 10.1504/IJOR.2022.10052526
   
  
• Effect of Quadratic Price-Dependent demand with Quadratic Time-Dependent Demand in EOQ Inventory Models for Deteriorative items  
  by Selvaraju P, Sivashankari C.K. 
  Abstract: This research focuses on impact of quadratic price-dependent and time-dependent demand in EOQ inventory models for deteriorative products in higher-order equations is examined in this article. Linear, constant, exponential, quadratic, stock dependent, price dependent, and other demand models have been discovered in the literature. In real practice, the price of the item and the time it takes to sell has a significant impact on the demand rate. Three models are developed: Quadratic time-dependent and price-dependent demands are used in the first model. In second model quadratic-time dependent and in the third model quadratic price dependent demands are used. The aim of this study is to identify the optimum cycle time and the optimum quantity that minimises the total cost. Each model has its own set of mathematical models. A sensitivity analysis is performed after solving and studying many numerical examples. Visual Basic 6.0 was used to create the required data.
  Keywords: EOQ inventory; quadratic price-dependent demand; quadratic time-dependent demand; integrate; optimality; sensibility analysis.
  DOI: 10.1504/IJOR.2022.10053350
   
  
• A Vendor-Managed Inventory Model for Deteriorating Products  
  by Anyarin Sakrujiratham, Huynh Trung Luong 
  Abstract: This paper develops a vendor-managed inventory model for deteriorating products in a two-level supply chain which is comprised of one vendor and one retailer in the case when the time to deterioration of the product follows Weibull distribution. It is assumed that the market demand is price-sensitive and shortages are fully backlogged. The proposed inventory model helps to determine the replenishment cycle length and the optimal replenishment quantity to help minimise the total cost of the entire supply chain. Numerical experiments and sensitivity analyses are conducted to illustrate the applicability of the proposed model. Some future research directions are also discussed.
  Keywords: vendor-managed inventory; VMI; inventory control; deteriorating products; supply chain management.
  DOI: 10.1504/IJOR.2022.10053513
   
  
• A novel hybrid BSC-DEA model for performance assessment in knowledge enterprises using balanced scorecard and data envelopment analysis approach  
  by Bakhtiar Ostadi, Masoud Sadri, Ehsan Nikbakhsh 
  Abstract: The importance of knowledge enterprises (knowledge-based companies) in countries’ economies and their role in GDP has recently increased, and many efforts have been made to achieve a comprehensive and consistent benchmark and model for evaluating these companies. Therefore, the purpose of this paper is to provide a hybrid model for performance assessment in knowledge enterprises. So, the primary indicators have been extracted by reviewing the literature and structure of knowledge enterprises. After collecting data from knowledge enterprises and combining the balanced scorecard (BSC) and data envelopment analysis (DEA) approach, a hybrid BSC-DEA model developed to assess the partial efficiency of each unit and the total efficiency of each knowledge enterprises. Finding mentioned that the ability of start-ups and knowledge enterprises to be compared with large and old ones. Also, there will be no significant difference in the performance of companies with respect to their type.
  Keywords: performance assessment; knowledge enterprises (knowledge-based companies); data envelopment analysis; DEA; balanced scorecard; BSC.
  DOI: 10.1504/IJOR.2023.10053850
   
  
• Optimizing Production and Operational Cost in a Limestone Mine by MINLP Approach: An End-to-End Case Study  
  by Anindita Desarkar, Aaditya Umasankar, Viswa Janith Paidisetty, Abhishek Sarma, Santosh Kumar Annabattula Venkata Varaha, Vishwanathan Raman, Mahesh Mahajan 
  Abstract: Prediction is always a challenging task; it gets harder especially in mining where lots of complexities and uncertainties are present in the system. Optimizing the production output by adhering to the ore quality, minimizing fuel consumption towards operational cost reduction, maximizing utilization and minimizing the idle time of the fleets are a few major goals in the mining industry. However, all these things depend upon the optimal distribution of resources and equipment in appropriate places. Though manual allocation can be one solution, but optimal result is not always achieved because it's quite difficult to optimize so many parameters on a day-to-day basis. The present research proposes a multistage and multi-objective optimization approach based on mixed integer non-linear programming to achieve the aforesaid goals. The experimental results show the efficacy of the method, and it is also implemented in one real mine scenario where all the above-mentioned goals are achieved.
  Keywords: Optimization; Mixed-Integer Non-linear programming; Production maximization; Fuel minimization; Resource allocation; Utilization; Productivity; Truck dispatching.
  DOI: 10.1504/IJOR.2023.10053952
   
  
• Waste Management by Bilevel Optimisation: A Survey  
  by Massimiliano Caramia, Emanuele Pizzari 
  Abstract: Waste Management is a complex and broad field of research. In problems falling under this category, several decision-makers have conflicting objectives and hierarchies. Therefore, the common approaches of single-objective optimisation or multiobjective optimisation may fail to capture the nuances of the situation. Hierarchical problems are best handled from a mathematical optimisation point of view via bilevel programming. In this paper, we survey contributions modelling waste management issues employing bilevel optimisation, a relatively new yet promising field of research. We start by providing a general analysis of these contributions and then describe the latter in macro-subjects. Finally, we draw some conclusions by providing open problems and follow-ups.
  Keywords: Bilevel optimisation ; Waste management ; Literature review.
  DOI: 10.1504/IJOR.2022.10054097
   
  
• A Production Model for Deteriorative items with Time Dependent Demand and Possible Adjustment of the Production Rate  
  by Sivashankari C.K., Valarmathi R. 
  Abstract: In this paper, two different rates of production problem of production inventory system for deteriorative items having constant demand, linear demand as well as quadratic demand is considered and in order to cut costs, it is preferable to begin production at a low rate (X1) and gradually increase to a higher rate (X2) over time. This is because starting with a low rate of production prevents an excessive quantity of manufactured goods from being stored at the outset. The variability in production rate offers a means both of resulting in the happiness of customers and of generating possible profit. There will be three models created. There is a consideration of demand that is constant demand in the first model, demand that is linear demand in the second model, along with quadratic demand in the third model. In all models, triangular inequality is used for evaluating production time and Bernoulli’s integration is used for integration in all three models.
  Keywords: Constant; Linear and Quadratic Demands; Two Rates of Productions; Optimality and comparative study.
  DOI: 10.1504/IJOR.2023.10054233
   
  
• Multi-objective optimization of surplus food recovery and redistribution units in India  
  by Nistha Dubey, Ajinkya N. Tanksale 
  Abstract: Food banks are not-for-profit organizations that collects surplus and leftover food and distribute it to unfortunate people of society with an aim to alleviate hunger. The problem can be modeled as multi depot-VRP. We endeavour three primary objectives of food banks - efficiency, effectiveness, and equity. The measure for efficiency is minimum total transportation cost, minimum total shortage for effectiveness, and minimum of the maximum shortage of network is taken for equity. This paper proposes a MILP model for multi-objective optimization of surplus food recovery and redistribution in India. Our study is the first to evaluate Indian food banks from a multi-objective perspective. To solve the proposed problem, state-of-the-art-solver Gurobi is used for weighted sum method, augmented ?-constraint method, and augmented weighted Tchebycheff methods. Non-Sorted Genetic Algorithm is developed to solve the larger network problems. The results of the computational experiments show significant trade-off behavior between efficiency and effectiveness.
  Keywords: Food banks; VRP; Multi-depot; NSGA-II; Multi-objective; Split loads.
  DOI: 10.1504/IJOR.2023.10055079
   
  
• Interaction Model Development in Determining House Prices by Using Goal Programming  
  by Nerda Zura Zaibidi, Nor Syuhaddah Saiddin, Adyda Ibrahim, Siti Aisyah Saupi 
  Abstract: The buyer, the real estate developer, and the government are typically the three main parties engaged in housing projects. The interaction between these parties affects the housing market, particularly the prices of homes. The interaction has become more difficult because of the disparities in preferences between the parties. The ideal strategy for creating a fruitful partnership between these parties is still a mystery. As a result, this study has established a decision maker interaction model for getting mutual understanding on a housing project. Goal programming and a simulation method were used in this work to develop a successful interaction model. The average dwelling price that all the parties had mutually agreed upon was represented by the mean value of RM 169,878 and it is skewed between RM 85,000 and RM 350,000. The results of this study can be used by developers in Malaysia to design homes that are affordable and appealing to buyers, preventing problems with long-term unsold homes.
  Keywords: interaction model; house prices; multi-objective optimisation; goal programming.
  DOI: 10.1504/IJOR.2023.10055108
   
  
• Genetic and Hybrid algorithms to solve the container stacking problem at Tripoli-Lebanon seaport.  
  by Nobar Kassabian, Zakaria Hammoudan, Olivier Grunder, Lhassane Idoumghar 
  Abstract: Several factors determined the survival of the seaport: logistics, storage and distribution. A storage strategy dependent on container stacking rules is an important factor in the competence of the container terminal. This article focuses on solving the problem of stacking incoming containers in the storage yard, taking into account several criteria regarding the port of Tripoli-Lebanon. A mathematical model with a mixed integer linear program for the container stacking problem is considered in this paper. As this problem is NP-hard, large instances cannot be solved by optimisation solvers as Gurobi. We develop four algorithms to tackle this problem: a genetic algorithm (GA), a randomised greedy algorithm (RGA), an iterated local search (ILS) and a hybridisation approach between RGA and ILS. Finally, numerical simulations prove the efficiency of the GA which produces results close to the optimal solution on real instances taken from the containers terminal for small and medium sizes.
  Keywords: container stacking problem; CSP; mathematical modelling; optimisation; Gurobi optimiser; genetic algorithm; GA; randomise greedy algorithm; RGA.
  DOI: 10.1504/IJOR.2023.10055823
   
  
• Metaheuristic-based approaches for the multi-centre open home healthcare routing problem  
  by Bilal Kanso, Ali Kansou, Adnan Yassine 
  Abstract: This paper presents the multi-centre open home healthcare (MC-OHHC) problem with time windows and synchronisation constraints. The MC-OHHC can be described as the problem of designing least cost routes from several centres to a set of visits, without forcing them to return to the centres. Some services require simultaneous visits by using different routes to be accomplished. The contribution of the paper is three-fold: 1) it presents the corresponding mathematical linear model; 2) it gives the results related to the CPLEX resolution and an adapted constructive heuristic solution of such a problem; 3) it provides the results related to a variable neighborhood descent algorithm, a simulating annealing algorithm and a hybrid genetic algorithm. Computational results on adapted set of benchmark instances from the literature are reported and show that our proposed approaches are fast, efficient and competitive compared to the solutions provided by the CPLEX software. Some optimal solutions are provided in short computational times, and greatly improve the initial solutions obtained by the proposed efficient constructive method.
  Keywords: home healthcare problem with multi-centres; window time; synchronisation; constructive heuristic; variable neighbourhood descent metaheuristic; simulating annealing metaheuristic.
  DOI: 10.1504/IJOR.2023.10056210
   
  
• Energy Demand Forecasting Using A Novel Optimised Fourier Grey Markov Based Approach  
  by Noorshanaaz Khodabaccus, Aslam A. E. F. Saib 
  Abstract: Energy supply affects the sustainable development of an economy, hence making its modelling and forecasting crucial to policymakers. Conventional statistical models often require either prior assumptions on the distribution of the data or large historical datasets. This paper proposes the optimised Fourier-Markov grey model (OFGM), which alleviates the former two assumptions. Two test scenarios are proposed for assessing the model's performance: data prior to the COVID-19 pandemic (20102019) and data extending over the pandemic period (20102020). Numerical experiments demonstrate that the proposed algorithm very well models both scenarios and a significant improvement in the prediction accuracy is achieved.
  Keywords: grey prediction model; Fourier; Markov; metaheuristic algorithm; energy forecasting.
  DOI: 10.1504/IJOR.2023.10056840
   
  
• The Reduction of Realized Variance in Deductible Insurance  
  by Christopher Gaffney 
  Abstract: We derive a series of mathematical identities that connect insurance purchasers with insurance companies. In particular, we focus on the way in which variance is shared between the parties. We argue that, from the perspective of governmental oversight, a desirable property of insurance is that the total amount of variance experienced by the involved parties is smaller under an insurance contract than in the uninsured case. It is shown that this always holds in the case of a single insurer and a single insured, while for the case of a single insurer and multiple insured, we derive a condition which guarantees the relationship.
  Keywords: deductible insurance; Affordable Care Act; ACA; insurance coverage; mean-variance analysis; variance reduction.
  DOI: 10.1504/IJOR.2023.10056842
   
  
• A State Dependent Arrival Analysis In a Non-Markovian Bulk Queue with Server Failures  
  by Palaniammal S, Pradeep S 
  Abstract: Breakdown brings a huge impact in the queueing system which causes complicated consequences. This paper comprises the results of functioning and malfunctioning of the queueing system due to continuous server breakdown. This work examines the failure of the server without interruption in state-dependent arrivals and numerous vacations. Even if a failure happens, the Server is not stopped for maintenance before finishing a batch of service. The queue size PGF at an irrational time period, as well as the probability generating functions of vacation, service, and renovation completion epochs, are derived using the additional variable technique. The queueing system's unique qualities and key features are provided, along with a cost model. An extensive numerical research is done using real-world examples.
  Keywords: state dependent arrivals; server breakdown; supplementary variable method; queue; bulk service; multiple vacations; cost model.
  DOI: 10.1504/IJOR.2023.10056844
   
  
• Performance analysis for F-policy machine repair problem with unreliable server balking, working breakdown and retention  
  by Sreekanth Kolledath, Kamlesh Kumar 
  Abstract: In this paper we study the controlled arrival of machine repair problem with balking, working breakdowns, reneging, and retention of failed machines. Failure times and service times of operating machines are assumed to follow the exponential distribution. When the service station works in normal mode, it is subject to breakdowns; while a breakdown occurs, the service station requires repair by the repairing facility. The service station’s breakdown and repair times are also presumptively exponentially distributed. Additionally, it is assumed that during a breakdown period of the service station, the service station may allow to provide service to the failed machines with slower service rate. The Runge-Kutta method (4, 5) has been employed to obtain the transient behaviour of the machine repair model. Several system governing performance measures are calculated. A cost function is constructed and also the sensitivity analysis is performed to explore the effect of different parameters.
  Keywords: unreliable server; F-policy; working breakdown; balking; retention.
  DOI: 10.1504/IJOR.2023.10057145
   
  
• Impact of Time-Dependent Environmental Factor on Software Release Planning  
  by Vibha Verma, Sameer Anand, Hoang Pham, Anu G. Aggarwal 
  Abstract: Reliability assessment of software during operational phase is critical for release and warranty decisions because users are concerned about software performance during usage period. To characterise the distinctions in the settings during the testing and operational phase an environmental factor is introduced in release planning problem. It represents the impact of the usage frequency on software performance and the relative severity of the phases. In this paper, the effect of a constant environmental factor (during the warranty phase) and a time-dependent environmental factor (during the post-warranty phase) on reliability and release decisions have been analysed. A software cost model has been formulated that minimises the development cost and determines optimal variable values (testing and warranty time) while achieving reliability requirements incorporating environmental factor in the release model. The impact of changes in reliability requirements cost components, and environmental factor on the release schedule has been studied using real-life fault datasets.
  Keywords: software release decisions; time-dependent environmental factor; warranty phase; testing phase; operational phase; development cost.
  DOI: 10.1504/IJOR.2023.10057646
   
  
• An approach for solving fully fuzzy linear fractional transportation problem with the using of splitting technique  
  by Sapan Das, Rajeev Prasad, Tarni Mandal, S.A. Edalatpanah 
  Abstract: This paper deals with an application of splitting technique to LFP problem including fuzzy coefficients (FC). This article mainly establishes and applies a modified form of splitting technique and ranking function for solving fully fuzzy linear fractional programming (FFLFP) problem. Here, we propose a method for solving for solving FFLFP problem with the help of splitting technique. After utilising the splitting technique, the problem is converted into equivalent fully fuzzy non-linear fractional programming (FFNLFP) problem and solved the problem with the help of ranking function. The proposed algorithm is tested with three types of problems. A real life diet example (data was collected from TATA-Main Hospital, Jamshedpur, India) 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: fully fuzzy linear fractional programming; FFLFP; crisp non-linear programming; fuzzy arithmetical; ranking function.
  DOI: 10.1504/IJOR.2023.10057875
   
  
• Optimal Inventory and Pricing for EOQ Inventory Models with Price-Dependent Demand and Exponential Demand  
  by Sivashankari C.K., Nithya T 
  Abstract: In the present work, an optimal lot size and optimal pricing with price-dependent and exponential demand for deteriorative items in third order equations is developed and also a special case for predetermined price is also considered. Optimal lot size and price are two decision variables in this paper and optimal cycle time is a decision variable in special case of this paper. The breakeven price is considered and the law of demand is proved. There are two models designed: the first model utilises an inventory model with optimum output and price in third order equation and the second model uses optimal cycle time of an inventory model for determining the price-breakeven point. But to my knowledge, no authors developed models for optimal pricing, and optimal lot size policies in price dependent and exponential demand in a third-order equation. This aims to obtain optimal lot size as well as pricing for overall maximum profits. The essential, as well as sufficient mathematical models are developed. Several examples, numerical in nature, are offered to achieve model validation. Additionally, a sensitivity analysis is carried out in conjunction with the representation's building blocks. Microsoft Visual Basic 6.0 was used to program the model's outcome validation.
  Keywords: EOQ; optimality; price-dependent demand; exponential time-dependent demand; sensitivity analysis; cycle time.
  DOI: 10.1504/IJOR.2023.10058103
   
  
• Inventory Control Optimisation: the Dynamics of Deterministic Request Model of Pharmaceutical Appropriation and Storage  
  by Adedugba Adebayo, Daniel Inegbedion 
  Abstract: The paper presents a stock control constraint at a drug manufacturing facility in Lagos State, Nigeria. The echelon of the organisational inventory chain was evaluated. The study developed a two-situation deterministic stock model that is based on realistic assumptions and necessities within the inventory network in order to conceptualise the situation within the model. A single medicine item, build lead time, and a definite request are taken into account in the formulation. The contingent request is handled as a forecast for the item’s sales and the optimisation between the recognised stock, the scheduled average stock level, and the reduction of stock-out situations are two mathematical functions. This was done in order to conclude the suggested mathematical formulation, which depends on a particular approach for selecting how to arrange the echelon. This is based on the paradigm of inventory and request. Therefore, optimality is achieved as a control mechanism.
  Keywords: control; inventory chain; request; optimality; pharmaceutical organisation; models.
  DOI: 10.1504/IJOR.2023.10058197
   
  
• Data Mining Techniques and Mathematical Models for the Optimal Problem at a State Public University  
  by Lijian Xiao, Shuai Wang, Xinhui Zhang 
  Abstract: This paper studies the optimal allocation problem of financial aid: the allocation of the appropriate levels of scholarships to the correct students, as observed in a state university. This research applies data mining techniques and mathematical models to solve the optimal financial aid allocation problems in three steps. First, data mining techniques, such as logistic regression, are used to determine the matriculation and graduation probabilities associated with students from various socioeconomic backgrounds and given levels of scholarship. Second, based on the responses to the different scholarship levels, an integer programming model is developed to maximise revenue over the students’ course of study. Third, decision tree and piecewise linear regression methods are employed to transform the results from the optimisation model into effective policies for implementation. This research has led to a scholarship redesign, and a straightforward scholarship award policy, based on a composite GPA and ACT score, has been implemented.
  Keywords: financial aid allocation; optimisation; data mining; logistic regression; integer programming; decision tree.
  DOI: 10.1504/IJOR.2023.10058404
   
  
• Data-Driven Approaches for Decision-Making in Advanced Manufacturing Systems: A Systematic Literature Review  
  by Vimlesh Kumar Ojha, Sanjeev Goyal, Mahesh Chand 
  Abstract: Rapid automation in advanced manufacturing systems enable them to capture, store and analyse data and adopt data-driven decision-making techniques. This study investigates the applications of data-driven techniques like big data analytics, AI, and ML in advanced manufacturing systems for decision-making. The paper identifies the various factors that affect the adoption of data-driven manufacturing techniques and reviews the framework strategies for their adoption. Applications of data-driven techniques in manufacturing, such as predictive maintenance, fault analysis, forecasting, and quality improvement, are discussed in detail. The authors also highlight the challenges associated with implementing data-driven decision-making (DDDM) in the manufacturing industry, such as data quality, privacy concerns and skilled workforce requirements. The study concludes that DDDM in AMS increases productivity, reduces operational costs, improves manufacturing operations and increases competitiveness. However, further research is needed to address the identified challenges and develop effective DDDM implementation strategies in AMS.
  Keywords: big data; IoT; decision-making; manufacturing; data analysis; automation; industrialisation; systematic review; data-driven decision-making; DDDM.
  DOI: 10.1504/IJOR.2023.10058496
   
  
• Analysis of Copula Based Variable Clustering Techniques and Application of Mortality Estimation  
  by Zeynep Ilhan Taskin, Veysel Y?lmaz, Kasirga Yildirak 
  Abstract: This paper aims at developing different mortality estimation models in MIMIC-III data set. One of the aims of the study is to bring an efficient technical proposal to determine the dependency structures between the variables. The study is conducted with 38015 adult intensive care patients in the MIMIC-III database. The dependency structure between the variables is determined and divided into clusters with CoClust and tail dependency. With Logistic Regression Analysis applied through clusters, the number of significant and appropriate models for death variable within 24 hours was four while there were five for death variable in the hospital. When the obtained models were analysed with error matrix, cross validity criterion and ROC curve, three valid models were obtained for the death variable within 24 hours and two for the death variable in the hospital.
  Keywords: Copula; CoClust; Clustering with Tail Dependency; Logistic Regression Analysis; Mortality Estimation.
  DOI: 10.1504/IJOR.2023.10058499
   
  
• A MODEL ON AN OPTIMAL ORDERING POLICY FOR DETERIORATING ITEMS WITH EXPONENTIAL DECLINING RAMP-TYPE DEMAND, VARIABLE DETERIORATION AND SHORTAGES  
  by Sephali Mohanty, Trailokyanath Singh 
  Abstract: The main objective of the proposed paper is to extend Sanni and Chukwu’s (2013) model with the incorporation of the following characteristics: 1) inventory system deals with a single type of item; 2) demand is a generalized demand pattern and is an exponential declining ramp-type function of time; 3) deteriorating items follow a variable deterioration rate where deterioration rate is a linear increasing function of time; 4) shortages in the developed system are assumed to be a natural phenomenon; 5) only complete backlogging case has been taken into consideration. The demand rate is deterministic: it varies with respect to time up to a certain fixed point, becomes steady and then, it is fully backlogged. A couple of numerical examples are used to study the effectiveness of decision variables in the model. Finally, sensitivity analysis of the optimal solution with respect to several system parameters of the model is examined.
  Keywords: completely backlogged; deteriorating items; EOQ; exponential declining ramp-type demand; shortages; variable deterioration.
  DOI: 10.1504/IJOR.2023.10058544
   
  
• Optimizing End-of-Life Laptop Remanufacturing Decisions Using Meta-Heuristics  
  by Gurunathan Anandh, Shanmugam Prasanna Venkatesan, Mark Goh, Gyan Chandra Kushwaha 
  Abstract: As the lifecycle of a laptop gets shorter, the world should expect more end-of-life (EOL) laptops. Remanufacturing of laptops is viewed as the best EOL alternative for environmental and societal reasons. This research uses a multi-period nonlinear integer programming model to decide the best EOL options for the remanufactured laptop parts based on their quality. Discrete particle swarm optimisation (DPSO) and genetic algorithm (GA) are implemented as a decision support tool in Microsoft Excel to yield the near-optimal solution. Numerical tests are conducted to compare the effectiveness of the two algorithms. For small-sized problems, the solution of the algorithms is compared with the global optimal solution obtained by the full enumeration method. For large problem instances, the solution obtained using the algorithms is compared with each other. A sensitivity analysis is performed to study the impact of the shortage and repair costs and demand on profit.
  Keywords: WEEE; end of life laptop; remanufacturing; discrete particle swarm optimisation; DPSO; genetic algorithm; GA; nonlinear integer programming.
  DOI: 10.1504/IJOR.2023.10058823
   
  
• Comparing classical time-series models and machine learning for demand forecast on the beverage industry in COVID-19 pandemic.  
  by Ana Camilla Macedo, Caio B. S. Maior 
  Abstract: Due to the growth of competitiveness in the market, demand forecasting has become a fundamental tool to manage production and identify new opportunities for the company. The fundamental goal of a series analysis is to make predictions from historical data to support decisions accurately. During the COVID-19 pandemic, the market has undergone numerous changes, and consumer needs have changed, directly affecting beverage sales. In this work, classic models of time series Holt-Winters and ARIMA and machine learning support vector machines and random forests were used to perform demand forecasts from several historical data series of a real beverage direct distribution centre located in Brazil. The data used were stratified into nine data series: 1) the total volume of beverages sold by the operation; 2) separated by type of beverage (beer and non-alcoholic beverage); 3) in six sales channels. Indeed, as the comparison considers demands before and after the pandemic (including pre-and post-vaccination), the predictions were challenging. The comparison of models considers predictions up to 15 steps (months) ahead using the RMSE and MAPE error metrics. Here, the models with the best-aggregated performances were ARIMA and SVM; however, no model was strictly better than the others.
  Keywords: time series; demand forecast; beverage industry; Holt-Winters; ARIMA; support vector machine; SVM; random forest.
  DOI: 10.1504/IJOR.2023.10059084
   
  
• A Two-Phase Extended Warranty Strategy for New and Reman Products  
  by Jalapathy P, Mubashir Unnissa Munavar 
  Abstract: In recent decades, waste management has attracted the attention of a substantial number of scientific and industrial firms, which paved the way for reman products. Also, reman product reliability increases significantly in the product market, and offering a warranty is the most efficient way to identify the product's quality and dependability through market sales. In this paper, a two-phase extended warranty model is offered for a new and reman product to analyse the pricing strategy of the monopolistic manufacturer. The paper develops a model framework to examine optimal prices, demands, and profits of new and reman products with an extended warranty by using the Karush-Kuhn-Tucker (KKT) condition. Further, to highlight the impact of the extended warranty, failure rates, and customer willingness on new and reman products, a numerical analysis is performed. The results reveal an insight on providing an extended warranty increases the manufacturer's profit.
  Keywords: re-manufacturing; pricing strategy; extended warranty; customer utility; profit analysis.
  DOI: 10.1504/IJOR.2023.10059156
   
  
• Analysis of MMAP/PH(1), PH(2)/1 Non-Preemptive Priority Queueing Model with Phase-Type Vacation and Repair, Feedback, Breakdown, Closedown and Reneging  
  by Ayyappan G, Meena S 
  Abstract: We consider a single server non-preemptive priority queue with phase-type vacation and repair, feedback, breakdown, close-down, and reneging. Customers arrive according to the marked Markovian arrival process and their service time according to phase-type distribution. If the high priority customers need feedback, they lose their priority and join the low priority queue. At any instant, if the server is broken down, it will immediately go into a repair process. When there are no customers present in both the queues, the server close-down the system and then goes on vacation. During the close-down and vacation period, high priority customers may renege. The matrix analytic method is used to look into the number of consumers that are currently in the system. Analysis of the steady-state, the server active period, and the total cost are all discussed. Finally, some significant performance measures and numerical examples are given.
  Keywords: marked Markovian arrival process; phase-type distribution; server vacation; breakdown; repair; feedback; close-down; reneging; non-preemptive priority; matrix-analytic method.
  DOI: 10.1504/IJOR.2023.10059338
   
  
• Fluid approximation for a Markovian queue under disaster and reboot  
  by Mayank Singh, Madhu Jain 
  Abstract: A fluid approximation for the performance analysis of a Markovian disaster queue with reboot and repair is presented. During normal operation, the system may suffer disaster failure, in which case all jobs in the system will be lost. If the fault is successfully covered, the system recovers from the failure by rebooting; otherwise, the system enters a repair state, where a specialised repairman removes the fault. An analytical methods of continued fractions (CFs) and probability generating function (PGF) are used to get the probability distribution of buffer content. To analyse the fluctuation in buffer content with regard to buffer content probabilities, the numerical data is computed and displayed in the form of graphs and tables. Furthermore, numerical results obtained using analytical formulae are compared with the results obtained by adaptive neuro-fuzzy inference system (ANFIS).
  Keywords: Markov fluid queue; disaster; reboot; continued fractions; ANFIS; probability generation functions.
  DOI: 10.1504/IJOR.2023.10059527
   
  
• MADEA: Multiobjective Amended Differential Evolution Algorithm  
  by Ishan Gawai, D.I. Lalwani 
  Abstract: The aim of the current work is to modify the amended differential evolution algorithm (ADEA) to solve multiobjective optimisation problems. The modified ADEA algorithm is named MADEA. The single objective ADEA algorithm is employed with an efficient non-dominated search (ENS) method for finding the non-dominated solutions, a crowding distance technique for comparing the non-dominated solutions, and an archive that stores the non-dominated solutions. The above-mentioned modifications in ADEA resulted in an algorithm capable of solving benchmark functions given in CEC 2009 with competitive results. The performance of the MADEA is measured using inverted generational distance (IGD) and hypervolume (HV). The outcomes of performance measures are compared against MWDEO, MOEA/D, MOPSO, SMPSO, NSGA-II, SHAMODEWO, MOEADSTM and NSGA-III. The results show that MADEA has outperformed 60% of the problems in the test suite in IGD values and the results were found to be significantly similar to 20% of the competition.
  Keywords: meta-heuristics; evolutionary algorithm; differential evolution; archives; multiobjective optimisation problems; amended differential evolution algorithm; ADEA; efficient non-dominated search; ENS.
  DOI: 10.1504/IJOR.2023.10059547
   
  
• Permutation flow shop scheduling with early and late penalty costs using the Jaya algorithm  
  by Raunaque Paraveen, M.K. Khurana 
  Abstract: Purpose of this study is to use the most efficient meta-heuristic methodologies in permutation flow shop to identify the ideal sequence of jobs with the least amount of penalties for being early and late. The permutation flow shop is a common job shop problem in which all jobs must pass through all machines in a predefined order. Numerous meta-heuristic algorithms have been developed to tackle this problem. However, users often struggle with selecting appropriate algorithm parameters due to the problem’s complexity. To address these challenges, this research adopts the recently developed Jaya algorithm, which stands out for being a parameter-less approach that aims to achieve success while avoiding failure. The Jaya algorithm was tested alongside a genetic algorithm using a simulated industry dataset. This dataset contained different scenarios with varying numbers of jobs and machines. The Jaya algorithm consistently outperformed the Genetic algorithm, providing superior results for the given problem.
  Keywords: permutation flow-shop scheduling; Jaya algorithm; tardiness penalties; earliness penalties.
  DOI: 10.1504/IJOR.2023.10059626
   
  
• Solving the multi depot vehicle routing problem with limited supply capacity at the depots with a multi phase methodology  
  by Javier De Prado, Sandro Moscatelli, Pedro Piñeyro, Libertad Tansini, Omar Viera 
  Abstract: We consider an extension of the multi-depot vehicle routing problem (MDVRP), in which the supply capacity of the depots is limited. To solve this problem, we propose a multi-phase methodology, that extends the
  Keywords: multi-depot vehicle routing problem; MDVRP; heuristics; limited supply capacity at depots; capacitated vehicles; clustering; assignment; routing; multi-phase methodology; MPM.
  DOI: 10.1504/IJOR.2023.10059627
   
  
• Threshold neutrosophic set and its application to decision making in planning and construction industry  
  by Tuhin Bera, N.K. Mahapatra 
  Abstract: The basic motivation of the present study is to furnish a decision making approach in soft and neutrosophic environment. The methodology is based on the notion of neutrosophic cut set. Different kind of threshold neutrosophic set and their level soft set are innovated here. Then their inter relations are also investigated. The approach is further extended over soft and weighted neutrosophic set. Suitable solution algorithms are developed in both attempts and these are demonstrated to make a decision in planning and construction industry. The outcomes are analysed and the potentiality of the proposed method is claimed after comparing the results from existing study.
  Keywords: neutrosophic soft set; threshold neutrosophic set; level soft set; decision making approach.
  DOI: 10.1504/IJOR.2023.10059650
   
  
• Uncertainty Quantification and Global Sensitivity Analysis of an M/G/1 Retrial Queue with Bernoulli Schedule  
  by Khedidja Boughafene, Karim Abbas 
  Abstract: In this paper, we are interested in studying uncertainty quantification and global sensitivity analysis in retrial queueing models. Specifically, we investigate the M/G/1 retrial queue with priority customers, Bernoulli schedule and general retrial times. We develop a new methodology for integrating epistemic uncertainties into the computation of performance measures of retrial queueing models, where these measures are considered as functions of the input random parameters and approximated with polynomial chaos expansions. In order to perform global sensitivity analysis, we use Sobol’ indices which allow us to make an importance ranking of parameters. In addition, we characterise statistically several performance measures, given that distribution of the model parameter expressing the uncertainty about the exact parameter value is known. Furthermore, we use the Markov inequality to assess the risk induced by working with uncertain performance measures instead of that evaluated at fixed parameters. Several numerical results are provided and compared to Monte Carlo simulations ones.
  Keywords: Sobol’ indices; polynomial chaos expansions; epistemic uncertainty; uncertainty quantification; risk analysis; Monte Carlo simulation; retrial queueing model.
  DOI: 10.1504/IJOR.2023.10059728
   
  
• Optimizing inventory management: addressing constant deterioration and imperfect items with screening, time-dependent demand, and two-layer trade credit  
  by Chirag Trivedi, Mrudul Jani, .Dilip C. Joshi, Manish Betheja, Nakul Kumar Rawal 
  Abstract: Inventory models are frequently developed with products produced are of perfect quality The purpose is to create a model with uncertain supply may have a random proportion of defective items As a result, item inspection becomes critical in all situations, when products are vanishing current companies may use promotional tools to boost sales trade credit is a strategy that benefits both suppliers and retailers Hence, a two-level trade credit scheme in which the supplier offers a credit period to retailer and retailer provides to customers Inflation is the rate at which the prices for goods often affects the buying capacity of consumers and recent time value of money is calculated The primary purpose is to enhance the retailer's overall profit with respect to cycle time and is numerically solved using a devised algorithm Finally, sensitivity analysis is done on key parameters, and some managerial implications for the retailer are emphasised.
  Keywords: deterioration; imperfect items; screening; time-dependent demand; two-layer trade credit.
  DOI: 10.1504/IJOR.2023.10059878
   
  
• Solving a Practical Examination Timetabling Problem via Abductive Reasoning and Integer Programming  
  by Daniel Morillo, Nicol Solarte-Herrera, Maicol Narváez-Rincón, Rafael Rojas-Millan, Gustavo Gatica, Jesus Gonzalez-Feliu 
  Abstract: Scheduling of examination dates is a complex process that affects student satisfaction at higher education institutions. The literature refers to this as the timetabling problem. Formally, it consists in assigning a series of events to certain timetable blocks within a given time interval, limited by a set of constraints, some of which must be strictly adhered to (hard), while others are only desirable (soft). This paper proposes and validates a mathematical model for scheduling at a university in Colombia. The main paper’s contribution is that the model is aimed at improving student satisfaction compared to the scheduling performed previously by the university. The methodology is based on an abductive vision of operations research with five stages where a mixed-integer linear programming model was proposed, and it was validated in a real-life instance. The results show a 38.9% average reduction in contiguous exams.
  Keywords: abductive methodology; integer programming; timetable problem; exam scheduling problem; applied case.
  DOI: 10.1504/IJOR.2023.10059881
   
  
• Evaluation of Power Generation Units of a Thermal Power Plant Focusing on Sustainability and Technical Attributes  
  by Taquiuddin Quazi, Vivek Sunnapwar 
  Abstract: Coal is the primary source for generating power across the world and it is expected that this fuel's domination would last for a few more decades. Coal-fired power generation contributes substantially to pollution, negatively impacts the natural habitat, and hampers socioeconomic aspects of the economy. Hence, there is a need to investigate the performance of the power-generating units of thermal plants based on the evaluation criteria. In this study, the evaluation attributes related to technical, economic, social, and environmental aspects have been identified through the critical literature survey and interaction with the subject matter experts of the domain. Three power units of a coal-fired power plant have been evaluated using the hybrid MCDM framework. The results of the investigation highlighted that the social aspect is the most crucial and subfactors, namely risk related to safety, energy generation efficiency, and social acceptability are the significant ones. Also, the power unit C ranked first out of the three units under consideration. Finally, managerial, academic, and social implications are offered.
  Keywords: evaluation; coal-fired power generation; sustainability; coal technologies.
  DOI: 10.1504/IJOR.2023.10059961
   
  
• A novel inverse DEA-R model as for decision maker's preferences  
  by Javad Gerami, Mohammad Reza Mozaffari, Peter F. Wanke, Yong Tan 
  Abstract: In this paper, we present an innovative inverse data envelopment analysis (DEA) approach that incorporates ratio data. The proposed model simultaneously estimates the levels of inputs and outputs of decision-making units (DMUs) based on predetermined efficiency. Additionally, the model allows for assessing the levels of inputs and outputs according to the preferences of the decision maker (DM). The proposed model is nonlinear initially, but we transform it into a linear programming model. We demonstrate that the proposed model is always feasible. For the inverse DEA ratio-based (DEA-R) process, we adopt a two-step approach. Depending on the DM’s preferences, we can employ different models in the inverse DEA-R process when dealing with ratio data. To illustrate the effectiveness of our approach, we present two numerical examples in the paper.
  Keywords: data envelopment analysis; DEA; ratio data; DEA-R; inverse DEA-R; input/output estimation.
  DOI: 10.1504/IJOR.2023.10060052
   
  
• Aspiration Level based Multi-Objective Quasi Oppositional Jaya Algorithm to solve Multi-Objective Solid Travelling Salesman Problem with Carbon Emission  
  by Aaishwarya Bajaj, Jayesh Dhodiya 
  Abstract: This paper aims to analyse the steady-state behaviour of a bulk input general service queue with second optional service (SOS), balking, feedback, random system breakdowns, delay time, and repair time. After arriving at the queue, the customer has to decide whether to join or refuse to join the queue (balking). After completing the first essential service (FES), if a customer is not satisfied with it, he may choose to rejoin the system (feedback) or opt for SOS or depart from the system with a certain probability. From time to time, the server may face random breakdowns during FES and SOS, and we assume there is a delay time before the server starts the repair process. Moreover, the service times (FES and SOS), delay time, and repair time have a general distribution, while the breakdown time follows an exponential distribution. The steady-state probabilities are computed using the probability-generating function (PGF). Finally, numerical illustrations of performance measures are provided.
  Keywords: Multi-Objective Solid Travelling Salesman Problem; Aspiration Level; Carbon Emission; Multi-Objective Quasi Oppositional Jaya; CPLEX.
  DOI: 10.1504/IJOR.2023.10060053
   
  
• Role of time dependent Parabolic Demand for deteriorating items and Time Dependent Partial Back order in an EOQ model of seasonal fruits under intuitionistic fuzzy environment.  
  by Sriparna Chowdhury, Prokash Mondal, Sanat Kumar Mazumder, Pritha Das, Prof. Kajal De 
  Abstract: This study stimulates a major business factor for seasonal retailers and farmers that meet big goals by minimizing the total inventory cost of seasonal agro products. At the rising of every season, demands start with shortages, and it takes time to fully filed a prime time. After that, a fresh order is placed to meet demand and deterioration for the remaining time. So, the model designed here stands with its implementation in a manufacturing concern. Here, we introduced an economic order quantity (EOQ) model with a time-dependent parabolic demand and constant deterioration rates, shortages allowed with payment delays, and a partial backorder. For a more realistic feeling, the uncertainty occurs, the model is simultaneously constructed under a crisp and intuitionistic fuzzy environment; the trade credit policy up to stock-out time is planned for both cases. The numerical example and the sensitivity analysis have been done for both environments.
  Keywords: EOQ; Seasonal Agro product; Deterioration; Trade credit; Partial backorder; Intuitionistic fuzzy.
  DOI: 10.1504/IJOR.2023.10060190
   
  
• A novel fuzzy network ASBM approach based on DEA and DEA-R models for efficiency measurement in oil refineries  
  by Javad Gerami, Sahar Ostovan, Mohammad Reza Mozaffari, Peter F. Wanke, Yong Tan 
  Abstract: In this study, we develop fuzzy network data envelopment analysis (DEA) models based on the additive slacks-based measure (ASBM) model in the presence of undesirable output. In the real world, we encounter many cases where the data are inaccurate and ratios are involved simultaneously. In this regard, we propose two new models to evaluate the efficiency of Decision Making Units (DMUs) with a three-stage network structure in the presence of fuzzy inputs and outputs based on DEA and DEA-R models, by selecting two different strategies, external and internal. In the following, we apply the proposed approach to evaluate a set of oil refineries in Iran, and we present the results of the research.
  Keywords: Data envelopment analysis; Efficiency; DEA-R; Fuzzy network DEA; three-stage network.
  DOI: 10.1504/IJOR.2023.10060303
   
  
• Non-Convex Queue Time-bound Optimal Load Balancing  
  by Najeeb Al-Matar 
  Abstract: Cloud computing offers numerous advantages and flexibility, but also presents challenges in scheduling and load balancing. These challenges are particularly significant in distributed computing paradigms, and the addition of cloud technology on top of distributed systems further complicates the problem. The performance of cloud computing is significantly influenced by these factors. To optimise scheduling and load balancing, the proposed work introduces non-convex queue time-bound optimal load balancing, a set of nonlinear metrics that can be applied to both convex and non-convex surfaces. The work involves mathematical modelling an objective function with bounded constraints using queuing theory, applying non-convex optimisation techniques to minimise metrics like cost, error, and bandwidth. The results are evaluated using cloudsim metrics and analysed to improve performance. This mathematically modelled system provides a reliable and cognitive approach to optimising the cloud environment.
  Keywords: non-convex cloud; nonlinear cloud; cloud optimisation; cloud load balancing; cloud resource management; cloud non-Markovian; cloud queue.
  DOI: 10.1504/IJOR.2023.10060407
   
  
• Queueing Models of Machine Repair Problems with Control Policies: A Survey and Analysis  
  by Parmeet Kaur Chahal, Kamlesh Kumar 
  Abstract: : This article offers a thorough review and analysis of the studies done on queueing models of machine repair problems (MRP) with various control policies. These policies govern the arrival of failed machines and the service mechanism in the queueing systems. Due to their many practical applications, queueing models of machine repair problems have drawn significant interest from researchers across the globe. Considerable efforts have been dedicated to investigating this area of research and the present article provides a mathematical framework for queueing models of machining systems, which incorporates various control policies. Furthermore, we explore the potential applications of machine repair queueing models that apply control policies and give a tabular summary of the research works listed while taking into account the models attributes and methodology used in particular investigations.
  Keywords: Machine Repair Problem; standbys; N-policy; F-policy; Threshold recovery policy; Bi-level control policy; Triadic control policy.
  DOI: 10.1504/IJOR.2023.10060548
   
  
• Selection of Suppliers for Supplier Development : An Integrated FD-FIS Approach  
  by Dalvi Manojkumar, Vishal Bhosale, Anjali More 
  Abstract: The objective of this study is to evaluate and rank a group of suppliers for the execution of supplier development activities (SDAs) based on identified supplier development (SD) criteria. An exhaustive literature analysis revealed a total of 14 SD criteria, which were then divided into five major criteria in consultation with decision-making authorities (DMAs). Fuzzy Delphi (FD) is used to determine the weights of significant SD criteria, and the fuzzy inference system (FIS) is used for the evaluation and ranking of suppliers. Based on the opinions of DMAs, the significance of SD criteria and sub-criteria is finalised. This study unveils that suppliers past performance and strategic benefits emerged as the foremost major criteria when evaluating and selecting suppliers for the implementation of SDAs. For the first time, an integrated FD-FIS approach is proposed to rank a set of suppliers based on the weights of SD criteria and sub-criteria.
  Keywords: supplier development; SD activities; fuzzy Delphi; fuzzy inference system; FIS; supplier development activities; SDAs; decision-making authorities; DMAs.
  DOI: 10.1504/IJOR.2023.10060727
   
  
• A first augmented Lagrangian method for binary QCQP problems based on a class of continuous functions  
  by Nirakar Sahoo, Rupaj K. Nayak 
  Abstract: This paper addresses the solution of a nonconvex 0-1 QCQP using two faster convergent augmented Lagrangian methods (ALM) based on a class of continuous functions and a parameter free convexification method. The binary constraints are converted into a class of continuous functions and the nonconvex constraints are convexified by a parameter free method. The proposed algorithm is then tested on a set of QCQP problems available in the research of Zheng and co-workers. We also present the comparison results among relaxed versions of SDPs and some state-of-the-art ALMs and obtained an advantage over their counterparts.
  Keywords: Augmented Lagrangian method; QCQP; semidefinite relaxation; global optimal solution; SDPNAL.
  DOI: 10.1504/IJOR.2023.10060840
   
  
• A non linear mathematical model for optimal discount pricing strategies to maximize the stakeholders-perceived value: Mapping in Tagouchi loss function concept  
  by Bakhtiar Ostadi, Ali Abdollahi, Ali Husseinzadeh Kashan 
  Abstract: Customer-perceived value (CPV) is the difference between the prospective customer’s evaluation of all the benefits and costs of an offering and the perceived alternatives. In this paper, we introduce a new non-linear mathematical model for developing optimal discount pricing strategies using the Taguchi loss function and the concept of customer lifetime value (CLV). model aims to maximise the stakeholders-perceived value (SPV), which is calculated based on the customer's mentality towards the price before making a purchase decision. If the customer's mentality is greater than the amount paid, the perceived value for the chain store is used as the basis for SPV calculation; otherwise, the perceived value for customers is used. application of our model shown in a chain store. Using Taguchi pricing and discount model, managers can calculate product portfolio discounts and adjust their marketing strategies according to the specific strategies of the store and product portfolio for customer segments.
  Keywords: customer-perceived value; CPV; discount pricing; customer lifetime value; CLV; Taguchi loss function; discount pricing strategies.
  DOI: 10.1504/IJOR.2023.10060847
   
  
• Improved managerial decisions for fuzzy stochastic bi-objective linear programming models: A Trade-off ratio-based autonomized approach  
  by Arindam Garai, Santanu Saha, Kajal De 
  Abstract: Industry-derived fuzzy stochastic bi-objective linear programming (FSBOLP) models typically focus on a single major objective function. However, mathematical analysis based classical minmax method fails to distinguish between two competing objectives. This sometimes causes Pareto optimal solutions failing to meet with managerial goals. Additionally, to assume that both objectives in a FSBOLP shall attain the same highest standard is unrealistic. To overcome these limitations, the proposed amended minmax model uses the absolute difference operator. This pushes intermediate nondominated points closer to predefined reference levels, generating more desirable Pareto optimal solutions. Then this study frames one trade-off ratio-based autonomised optimisation algorithm, updating reference levels throughout iterations and reducing the need for frequent manager interactions. A numerical case study validates the effectiveness of proposed approach. This way, this study provides an improved framework for FSBOLP models, addressing the shortcomings of the traditional method and providing better decision-making support for industry managers.
  Keywords: fuzzy stochastic bi-objective linear programming; FSBOLP; preferred Pareto optimal solution; interactive optimisation; autonomised reference membership level; trade-off ratio; minmax method.
  DOI: 10.1504/IJOR.2023.10060979
   
  
• Impact of Slippage Cost and Risk Cost on Software Development under Imperfect Debugging Environment  
  by Neha Gondwal, Abhishek Tandon, Anu G. Aggarwal 
  Abstract: Computers have become an integral part of everyone’s life in this modern information society, and they are especially important in several fields that save lives. Consequently, it is necessary to develop reliable and cost-efficient software systems. This study discusses a cost model that integrates slippage and risk costs and looks at how these cost factors affect the timing of software releases. The reliability growth model, which is developed by taking into account the testing coverage function in an imperfect debugging environment, is used to study the cost model. The model is validated using a real-life failure dataset and with the estimated values, we formulate an optimisation model to develop the cost model concerning reliability function. With the aid of sensitivity analysis, the study also examines the potential effects of cost variation on the overall budget and delivery schedule.
  Keywords: software reliability growth model; SRGM; testing coverage; imperfect debugging; optimisation.
  DOI: 10.1504/IJOR.2023.10061339
   
  
• Dividend decision-making Algorithm Among Multi-Firms of Available and Required Capacity within Constrained Horizontal Coalitions  
  by P.P. Mishra, Chipem Zimik 
  Abstract: In this paper, our extended algorithm is used to achieve an optimum horizontal coalition among multi-firms of available and required capacity space. And also it calculates the Shapley values for profit distribution of all participating firms in the coalition. In this coalition, only suppliers are players and form subgames to manage the capacity space to achieve optimum profit. We establish the relationship between the core and the Shapley values of these subgames along with their geometrical interpretation. The numerical illustration is also shown for a better understanding of our model.
  Keywords: cooperative game; subgame; capacity space; horizontal cooperation; optimal coalitions.
  DOI: 10.1504/IJOR.2023.10061343
   
  
• Performance Analysis and Optimisation of Secondary Queue: A Study of Railway Passenger Reservation System  
  by S.M. QASIM, Jamal Ahmad Farooqui 
  Abstract: Every day, millions of people travel by rail in India. Even still, tens of thousands of waitlisted tickets are cancelled because there is no room on the chosen train on a given day. Such cancellations leave waitlisted ticket holders disgruntled and uncomfortable, costing the Indian Railways' passenger sector revenue. A secondary queue of waitlisted ticket holders occurs when the number of berths in demand is fewer than the number available in the requested accommodation class on a particular train on a specific day. The few customers from this line are served using confirmed tickets that have been relinquished by the person having confirmed tickets from the main line (primary queue). To validate the real queuing model, the study employs field data from the Centre of Railways Information System to establish the secondary queue's statistical distribution of arrivals and services. Furthermore, it analyses the secondary queue's performance characteristics using G/G/c/K queuing science and optimises the secondary queue design. This facilitates sensitivity analysis (changing values).
  Keywords: queuing in service operations; primary queue; secondary queue; queuing science; optimal design; Indian railways.
  DOI: 10.1504/IJOR.2023.10061608
   
  
• Inventory model for non-instantaneous deteriorating items with hybrid price and stock-dependent demand in the view of delay in payments, inflation, and customer returns  
  by Jayasankari Chandramohan, R. Uthayakumar 
  Abstract: This study creates an inventory model for a product that deteriorates gradually over time, with a hybrid price and stock-dependent demand that is affected by inflation and customer returns. We know from experience that demand is not necessarily constant, linear, or nonlinear; it is uncertain. The selling price, stock, and time value of money all influence demand. Shortages are allowed and partially backlogged. Customer returns are thought to rise in direct proportion to the portion vended and the product cost. The principle goal is to find the ideal cost structure, the time when there is no supply shortage, the appropriate restocking cycle, and the order amount all at the same time. The actual value of total profit is maximized over an indefinite time horizon.
  Keywords: Hybrid-price-stock dependent demand; Inflation; non-instantaneous deterioration; Delay-in-payments; Customer returns.
  DOI: 10.1504/IJOR.2023.10061609
   
  
• The Harris-Odd Burr Type X-G Family of Distributions with Applications  
  by Broderick Oluyede, Neo Dingalo, Fastel Chipepa 
  Abstract: This note introduces the new Harris-odd Burr type X-G (Harris-OBX-G) family of distributions and thoroughly investigates its statistical properties. These encompass essential features such as the quantile function, moments, moment generating function, the distribution of order statistics, and the Renyi entropy. To estimate the model parameters, we employ the maximum likelihood estimation technique, and we assess the reliability of these estimates through simulation studies. Furthermore, the versatility and practicality of the Harris-OBX-G family of distributions are demonstrated through its application to analyse two distinct real-world datasets from diverse fields.
  Keywords: Harris-G distribution; odd Burr type X-G distribution; simulations.
  DOI: 10.1504/IJOR.2023.10061696
   
  
• The Effect of Preservation Technology Investment on Inventory Model with Quadratic Demand Rate and Time dependent Holding Cost Under Two-Level Trade-Credit Policy  
  by Manoj Kumar Sharma, Mukesh Kumar, Satya Jeet Singh, Divya Mandal 
  Abstract: In this paper, we developed a deterministic inventory model that includes some genuine parameters, such as items that deteriorate over time, so we implemented a Weibull distribution rate that gives it a broader application scope. After that, preservation technology to reduce the rate of decaying items, and two levels of credit policy were applied to increase the demand rate. Also, we considered a quadratic demand rate, which is a function of time and time dependent holding cost. These parameters are reliable in economic terms. The model has also been verified via a numerical example (for all cases). The solution procedure and the sensitivity analysis have been discussed for each situation.
  Keywords: quadratic demand; preservation technology; two-levels trade credit policies; time-dependent holding cost.
  DOI: 10.1504/IJOR.2023.10061805
   
  
• A Markov Model for Prediction of Academic Manpower System in Nigerian Colleges of Education  
  by Shamsuddeen Ahmad Sabo, Nitesh Kumar Adichwal, Ibrahim Zakariyya Musa, Yakubu Isyaku Kibiya, Badamasi Abba, Syed Aqib Jalil, Akhil Damodaran 
  Abstract: This research employed a Markov model to assess the progression of the academic staff workforce in Nigerian colleges of education, using Sa'adatu Rimi College of Education, Kano as a case study. The objective was to underscore the importance of effective workforce planning in mitigating staff shortages or surpluses. By analysing transition probabilities derived from college planning department data, the study constructed a transition probability matrix depicting staff flow over time. The Markov model's findings projected an estimated 568 academic staff members for the college in the 2024/2025 session, distributed across academic ranks. Notably, the forecasted staff structure skewed towards higher ranks. Furthermore, the study offered insights into new staff members' anticipated years of service at different academic ranks. The research underscores the pivotal role of workforce planning in educational institutions, highlighting the Markov model's value in guiding staffing and workforce management decisions.
  Keywords: Markov Model; Prediction; Workforce; Stationarity; Transition Probability.
  DOI: 10.1504/IJOR.2023.10062344
   
  
• A numerical approach for fuzzy quadratic programming problems with an application to dairy farming  
  by Sumati Mahajan, S.K. Gupta 
  Abstract: Quadratic programming (QP) involving fuzzy parameters can be regarded as an extended version of conventional QP A vast variety of practical applications makes fuzzy QP (FQP) a preferred choice Fully FQP (FFQP) is a further generalisation of FQP by including fuzzy decision variables so as to avoid enforcement of crisp decision variables The study proposes a numerical approach using suitable weights attached to lower and upper value of the objective function after applying -cut in order to retain the fuzzy aspect of FFQP The equivalence between the new formulation and original model has been established through various theorems. The numerical examples are also solved to clarify the proposed method. Besides generalisation, the study brings forth its significance in real life applications viz. dairy farming problem, which is first modelled and then solved using the proposed algorithm.
  Keywords: Quadratic programming problems; Fuzzy decision variables; Numerical approach; Real life problem.
  DOI: 10.1504/IJOR.2023.10062450
   
  
• An improved Bombus-Terrestris bee optimisation algorithm implementation for enhancement of PQ in smart grid environment with hybrid renewable energy sources  
  by S. Mani Kuchibhatla, D. Padmavathi, R. Srinivasa Rao 
  Abstract: The escalating demand for distributed generating systems has driven the adoption of grid-connected hybrid renewable energy sources like wind, solar, and fuel cells. However, their integration into modern smart grid systems induces significant voltage fluctuations, impacting power quality and grid stability. This study proposes a hybrid optimal compensation scheme to enhance power quality in hybrid renewable energy systems (HRES) within smart grids. Employing a modified termite alate optimisation (MTAO) algorithm improves system performance. Addressing total harmonic distortion (THD) through suitable filtration techniques and assessing harmonic reduction efficiency, the study tackles THD issues in industrial distribution networks penetrated by renewable energy using a static switched filter compensation (SSFC) scheme. This approach aims to regulate voltage, curtail power loss, and minimise THD, thereby bolstering the overall performance and stability of hybrid renewable energy systems in smart grids.
  Keywords: hybrid renewable energy system; HRES; power quality; total harmonic distortion; THD; static switched filter compensation; SSFC.
  DOI: 10.1504/IJOR.2023.10062499
   
  
• A new optimisation model of the visitor routing problem applied to smart tourism  
  by Safae Rbihou, Nour-Eddine Joudar, Khalid Haddouch 
  Abstract: The visitor routing problem (VRP) is currently a major issue facing the tourism sector. The increasing flow of visitors demands efficient scheduling of their movement to ensure optimal visitor experience. In this paper, we propose a new mathematical programming model for managing tours for multiple groups of visitors based on their individual preferences. The proposed model takes into account various criteria such as distance, duration, budget, and risk factors. The well-known heuristic simulated annealing algorithm is well-suited to solve the proposed model. We applied the proposed routing model to a real-world problem related to the development of Fez city in Morocco. The experiments demonstrate that the proposed model offers numerous advantages for the development of the tourism sector.
  Keywords: Visitor routing problem; Mathematical programming; Tourism in Fez-Morocco; Heuristic; Simulated annealing.
  DOI: 10.1504/IJOR.2023.10062571
   
  
• Analysis the Performance of SDN Cloud Controllers Using Non-Markovian Queueing Models  
  by Najeeb Al-Matar 
  Abstract: Cloud computing is valuable for businesses, offering benefits like measured service. Implementing software-defined networks (SDN) instead of physical switches reduces hardware costs. SDN provides better control, scalability, and flexibility. Efficient SDN management is crucial for cost savings in cloud computing. Concerns with SDN include issues with data plane performance, ternary content addressable memory (TCAM), and controllers. Effective controller handling maximises throughput. Cloud computing generates high traffic volumes, with data and control traffics operating in different planes. Optimising traffic can enhance performance, and utilising queueing mechanisms can improve controller performance. Non-Markovian queueing models are proposed to handle heterogeneous buffers with varying traffic intensities. Models like M/M/1, M/M/n, M/M/s/n are used to manage traffic and improve controller performance. Both centralised and distributed systems are considered, taking into account behavioural distributions. Queueing models enhance QoS metrics, reducing waiting time, average queue intensity, and probability of idle server.
  Keywords: cloud SDN; SDN controllers; SDN controllers queue; SDN queue; SDN queue performance.
  DOI: 10.1504/IJOR.2024.10062717
   
  
• Scenario reliability assessment for CVaR minimization in two-stage stochastic programs  
  by Ghazal Shah Abadi, Sarah M. Ryan 
  Abstract: Reliable scenarios are needed to obtain a high-quality solution to a stochastic program. Considering sets of scenarios and corresponding observed values of the uncertain parameters over a collection of historical instances, reliability is defined loosely as goodness of the scenario's fit to the observations. For two-stage, risk-neutral models, a statistical tool was developed previously to assess the reliability of any given scenario generation method. This tool can diagnose over- or under-dispersion and/or bias in the scenario sets. For risk-averse decision makers who aim to minimise conditional value-at-risk (CVaR), only the scenarios that define the upper tail of the optimal cost distribution at the optimal solution are important. We develop a tool to assess the reliability of these so-called effective scenarios for CVaR minimisation. Simulation studies of a financial investment problem demonstrate the ability of the tool to detect mismatches in mean, variance, or kurtosis between scenarios and the corresponding observations.
  Keywords: risk-averse stochastic programming; conditional value-at-risk; CVaR; scenario reliability assessment; goodness of fit.
  DOI: 10.1504/IJOR.2023.10062719
   
  
• An Optimal Policy on a Two-Warehouse Model for Deteriorating Inventory Items with Varying Time-Dependent Generalised Demand and the Conditions of Delay in Payments  
  by Itishree Rout, Trailokyanath Singh, A.K. Nayak 
  Abstract: The objective of this research is the extension of extend Liang and Zhous (2011) two-warehouse inventory model by incorporating of the following characteristics: 1) the two-warehouse system considers only one type of items; 2) the grace period is fixed; 3) the supplier offers a grace period to the customer for setting the account; 4) deteriorating items follow the two different constant deterioration rates in two warehouses; 5) demand is a time-dependent generalised demand pattern and is a cubic function of time; 6) shortages are not allowed to occur in this system. The grace period is offered with the three main circumstances such as the grace period is less than the cycle length, the grace period is less than or equal to the cycle length, and the grace period is greater than the cycle length of the system. An easy-to-use solution procedure with the technique of maxima and minima are provided to determine the optimum total cost of the two-warehouse system. At the end, a couple of numerical examples along with sensitivity analysis of system parameters are illustrated. The adoption of time-dependent cubic demand reflects a real market demand for seasonal and newly launched products having short life span.
  Keywords: Deteriorating items; grace period; inventory; time-dependent generalized demand; two-warehouse system.
  DOI: 10.1504/IJOR.2024.10062826
   
  
• Approximate Solutions of Ordinary Differential Equations Modelling Queue Length Dynamics  
  by Zeina Mueen  
  Abstract: Investigation regarding queue length dynamics have been explored using various kinds of models including ordinary differential equations (ODEs). ODEs possess the advantageous property of having a continuous-time representation of queue length dynamics, thus its adoption in this article in modelling various scenarios as system of first-order ODEs. Specifically, a multi-server queue is considered and a numerical approach for solving first-order ODEs using MATLAB is adopted to obtain approximate solutions of the resultant models. The results display the dynamics of the queue length in each scenario, thus showing the usefulness of ODEs in analysing the behaviour of queues using system of ODEs.
  Keywords: first-order; multi-server; ordinary differential equations; queue length.
  DOI: 10.1504/IJOR.2024.10063068
   
  
• Enhancing Project Selection: Elicitation of Parameter Values for Effective Multiple Criteria Project Sorting  
  by Efrain Solares, Eduardo Fernández, Abril Flores, Reimundo Moreno-Cepeda, Raymundo Diaz, Edy Lopez Cervantes 
  Abstract: Effective decision making is crucial to the success of an organisation's project selection problem as it involves addressing complex challenges. One such challenge, known as multiple criteria sorting, is to assign projects into ordered classes considering the preferences of a decision maker (DM). The authors recently proposed a comprehensive method to address highly complex problems based on the outranking approach. However, it is widely acknowledged that determining parameter values for methods based on the outranking approach can be difficult. This difficulty stems from the large number of parameters involved and the DM's limited familiarity with them. Here, we focus on two aspects: 1) how to obtain the parameter values for the method; 2) how to incorporate imperfect knowledge during the sorting process. We adopt the preference disaggregation paradigm and employ evolutionary algorithms. Our proposed approach demonstrates excellent performance in a wide range of computational experiments.
  Keywords: Project selection; multiple criteria classification; parameter elicitation; evolutionary algorithms; outranking methods.
  DOI: 10.1504/IJOR.2023.10063107
   
  
• Application of Mixed-Integer Linear Programming Problems to Optimize Timetables and Classroom Arrangements  
  by Dinh Son Nguyen, Dinh Nhiem Le, Cong Hanh Nguyen 
  Abstract: The University of Danang-University of Science and Technology in Vietnam currently manages over 3,000 courses annually for more than 15,000 students. The task of scheduling and allocating classrooms for these courses is particularly challenging, given the limited availability of 127 classrooms across various buildings and the substantial number of 300 lecturers. As a result, many lecturers' teaching schedules are suboptimal. Therefore, this paper proposes a comprehensive mathematical model aimed at optimising timetables and classroom allocations. Furthermore, it suggests the development of an algorithm to solve this optimisation problem, enabling the automatic generation of timetables and classroom allocations using Python.
  Keywords: operations research; linear programming; integer linear programming; optimisation problems; MIP.
  DOI: 10.1504/IJOR.2024.10063244
   
  
• Optimizing meal delivery through travelling salesman problem and cluster analysis  
  by Erkan Kose, Danışment Vural, Merve Yılmaz, Gizem İnci, Pınar Nur Oral 
  Abstract: Food companies confront the challenge of ensuring timely meal deliveries to prevent spoilage, requiring the implementation of efficient distribution strategies. This study was designed to minimise the total distance and time travelled by a catering company in food distribution. Two distinct approaches are employed to optimise the delivery process. In the first approach, customers are grouped through cluster analysis, and optimal distribution routes for each group are determined using the traditional travelling salesman problem. Conversely, the second approach consolidates customers into a single group, examining scenarios with multiple vehicles. The multiple travelling salesman problem is then applied to determine optimal routes for each vehicle. Results from a practical case study demonstrate a noteworthy reduction in delivery time and total distance covered, underscoring the effectiveness of both approaches. By presenting numerical evidence, this study contributes valuable insights to the enhancement of meal delivery efficiency in the food industry.
  Keywords: mathematical modelling; cluster analysis; travelling salesman problem; multiple travelling salesman problem; meal delivery problem.
  DOI: 10.1504/IJOR.2024.10063345