International Journal of Operational Research (119 papers in press)

Regular Issues

• 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
   
  
• 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
   
  
• Bi-objective multiple drones and trucks combined operation parcel delivery  
  by Bhawesh Sah, Shalabh Singh, Rohit Titiyal, Sonia Singh 
  Abstract: Drones have the capability to work in co-ordination with trucks to improve the last mile delivery process. The traveling salesman problem with drone (TSP-D) is one such recently introduced problem, where drones and a truck work together to make deliveries. We propose a bi-objective drone truck combined operations model with multiple drones and trucks working out of a depot to make deliveries to customers. The two objectives considered are makespan completion time and the delivery cost, both of the objectives are of minimization type. We propose a Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve the proposed variant and present a thorough sensitivity analysis to discuss the efficacy and efficiency of our work.
  Keywords: Drone logistics; parallel drone scheduling travelling salesman problem; travelling salesman problem; time-cost trade-off.
  DOI: 10.1504/IJOR.2024.10063707
   
  
• An Inventory Models for Product Life Cycle for Deteriorating Items with Growth of Demand - a Comparative Statement  
  by Sivashankari C.K. 
  Abstract: A product life cycle is the life span of a product which period begins with the initial product specification and ends with the withdrawal from the market of both the product and its support. In all inventory models, a general assumption is that the demand rate is constant. But in real life, the demand rate fluctuates over the period. Growth of demand refers to an increase in demand over an extended period. Two different inventory models are developed for deteriorating items with: 1) the constant demand rate is studied first; 2) the growth of demand is investigated next. A comparative statement between constant demand and growth of demand is carried out The objective of this paper is to find the optimal cycle time, which minimises the total cost and optimal amount of shortage. The relevant model is built and solved numerical examples are furnished and justified.
  Keywords: mathematical models; product life cycle; PLC; growth of demand; production and deteriorating items.
  DOI: 10.1504/IJOR.2024.10063808
   
  
• Entrepreneurship Sustainability Prediction System: A study based on Climatic Changes  
  by Srinivas Subbarao Pasumarti, Ansumalini Panda 
  Abstract: The present study relates to a system and method for predicting entrepreneurship sustainability which aids to develop their social and environmental responsibilities towards the society and increase their own sustainability with the available and predicted resources. The system comprises a business data module (BDM), an environmental data module (EDM), an analysing module (AM), a ranking module (RM), and a suggestion module (SM). Thus, the entrepreneur is suggested with the chances and risks in the future with the available resources in a specific geometrical region or with the changes in the environment based on the ranking allocated.
  Keywords: entrepreneurship sustainability; prediction system; climatic changes; risks; decision-making; business data module; BDM; environmental data module; EDM.
  DOI: 10.1504/IJOR.2022.10064006
   
  
• A Location Analysis and Modeling Approach for Optimizing Public Access to an Automated External Defibrillator Network  
  by Trevor Angel, Lihui Bai, William Paul McKinney 
  Abstract: Sudden cardiac arrest (SCA) remains a major public health concern among industrialised nations, especially the US reporting approximately 350,000 SCAs annually. There is a clear need for strategic placement of AEDs within public spaces to promote more rapid treatment of SCA. We propose a multi-disciplinary method by using an optimum location model in conjunction with population health and ArcGIS block level data. Analytics reveal that certain population segments are at higher risk for SCA, and we propose a total risk-adjusted coverage value (TRACV) as the objective of the optimum location model. Data collected from PulsePoint, a public AED registry, for Boone County, Kentucky is used as a case study. Extensive solution and policy analyses are reported. The analyses suggest that simply purchasing more AED units unsystematically is not the best answer to improving AED access, thereby amplifying the significance of employing location analysis and modelling approach for public health planning.
  Keywords: analytics; automated external defibrillator; health services; location.
  DOI: 10.1504/IJOR.2024.10064237
   
  
• A Framework for Nurse Scheduling Problems Based on Prevent Bad-Relationships  
  by Mehboob Kanhio, Zain-Ul-Abdin Khuhro, Farhat Naureen Memon, Muhammad Saleem Chandio 
  Abstract: The scheduling problem is one of the most studied and well-known problems in mathematics. The scheduling problem is used to allocate resources in the most efficient way. In any hospital, scheduling the nurse's timetable has a big impact on their performance. Nurse scheduling is a complex problem that requires careful consideration of various factors, including skill heterogeneity, unfavourable relationship factors, and expenses. This research proposes a framework for nurse scheduling that uses integer linear programming (ILP) to minimise unfavourable relationship factors and reduce expenses. The framework is validated using a few well-known benchmark scenarios, and the results show that the proposed model performs as expected and will be helpful for management.
  Keywords: scheduling; staff preferences; job satisfaction; skill heterogeneity; healthcare; nurse scheduling problem; NSP; hard constraint; soft constraint.
  DOI: 10.1504/IJOR.2024.10064338
   
  
• Government subsidies for downstream innovation with cost-sharing under different power structures  
  by Chengli Wei, Hongzhuan Chen, Yuanfei Kang 
  Abstract: Our game model analysis reveals that subsidy schemes significantly impact downstream innovation with cost-sharing under different power structures in a two-tier supply chain. Specifically, government subsidies benefit R&D innovation, while customer subsidies increase members' profits regardless of power structure. However, the effect of R&D subsidies on individual members' profits varies. Manufacturer profits rise with the proportion of R&D subsidies, but only when the supplier lacks dominance in the supply chain. The impact on supplier profitability depends on the supplier's R&D investment share and government contributions to manufacturer R&D spending. The choice of subsidy scheme depends on the government's primary objective. Consumer subsidies effectively promote subsidised product growth and increase supply chain participant profits, while R&D subsidies aim to minimise government expenditure and optimise capital utilisation. Both subsidies are crucial for maximising R&D innovation levels.
  Keywords: government subsidies; cost-sharing; innovation; channel power; game theory.
  DOI: 10.1504/IJOR.2024.10064424
   
  
• An Optimisation Method for Ranking College Football Teams  
  by Mohsen Hamidi, Mehdi Seifbarghy 
  Abstract: This study presents a new optimisation method for ranking college football teams in the Football Bowl Subdivision (FBS). The ranking method is referred to as rating discrepancy minimisation ranking method (RDMRM). The RDMRM takes the strength of schedule, margin of victory, number of wins and losses, and conference championship factors into account. In this research, a rating discrepancy metric is defined, and a new mathematical optimisation model is developed to minimise the mean and standard deviation of rating discrepancies for all games. Incorporating the standard deviation results in balanced minimisations of rating discrepancies. To assess the reliability of this new ranking method, the RDMRM rankings are compared with the College Football Playoff (CFP) rankings. The comparison analysis shows that the results of postseason games are more consistent with the RDMRM rankings. The analysis also indicates that the RDMRM rankings are more proportional to the competitiveness of postseason games.
  Keywords: sports ranking systems; mathematical optimisation; college football; Football Bowl Subdivision; FBS.
  DOI: 10.1504/IJOR.2024.10064633
   
  
• Deteriorating Inventory Management Model: Adapting to Variable Lead Time and Consumer-Driven Shortages  
  by Atma Nand 
  Abstract: Nowadays, businesses work together and create supply chain alliances under a particular framework, such as vendor-managed inventory. The collaboration increases visibility, which reduces costs. It aids in enhancing the environmental performance of the supply chain. Inventory model suggested here helps managers choose the optimum inventory options while considering logistics costs. It elevates the provider to a dominant position, and clients are solely supplied by that vendor. Two models are presented in this research. In the first model, the vendor and customer are expected to make a non-collaborative decision. In the second approach, the merchant and the customer decide. This work proposes an iterative process for determining the best answer, which is then tested using numerical examples demonstrating the presented models' outcomes. The current research proposes a unique evidential theory-based technique for solving an inventory model with stochastic deterioration rates and lead times that appears in a single-vendor multi-buyer inventory system.
  Keywords: supply chain management; vendor-managed inventory system; integrated inventory model; merchant-consumers model; deterioration.
  DOI: 10.1504/IJOR.2023.10064634
   
  
• Exploring Reliability Factors in a Sequential Solar System: A Copula-based Investigation of Dependability Substances  
  by Abdulhadi Aminu, Abdul Kareem Lado, Ibrahim Yusuf 
  Abstract: Solar power is presently the most popular energy source compared to other energy sources. Therefore, companies all over the globe have been working hard to develop solar-powered devices to equalise the demand. According to the claim above, this study focused on a sequential multi-state solar system that involved five subsystems: a photovoltaic, a diode, a resistor 1, a resistor 2, and a voltage, which are all built in a series pattern. The exponential and copula schemes are expected to go hand in hand with system failure and repair. Partial differential equations were formed from the system's transition plot, analytical results were acquired using supplementary variable and Laplace conversion techniques. Dependability substances such as availability, reliability, mean time to failure, sensitivity, and cost were generated using the Maple software package. For validating the outcomes, an illustration of the numerical exhibits was included. Numerous system managers, including those in charge of system modelling, system reliability, system designers, and engineers, are expected to find the study's findings beneficial.
  Keywords: system; dependability; solar; factor; powered.
  DOI: 10.1504/IJOR.2024.10064704
   
  
• Robust Neutrosophic Modelling and Optimisation Approach for Solving Multi-objective Multi-Product Production Planning Problem  
  by G. Tamilarasi 
  Abstract: Neutrosophic set theory is a generalized version of fuzzy and intuitionistic fuzzy set theory, where membership degrees of acceptance, rejection, and indeterminacy are both measured. In this article, we formulate a multi-product production planning problem with multiobjectives with parameters as neutrosophic environments. The uncertain parameters are depicted as trapezoidal neutrosophic numbers, and the crisp version is developed by a new accuracy function. In the solution procedure, we have investigated interactive neutrosophic optimization techniques with neutrosophic parameters. Furthermore, various sorts of membership functions, such as linear, exponential, and hyperbolic, are depicted so that the marginal evaluation of each objective is determined. The proposed method represents an opportunity for select decision-makers with desired membership functions. To show the relations among the multiobjective multi-product production planning problem, its equivalent crisp formulation obtained by applying three different membership functions is established. Finally, the stepwise solution procedures are discussed, and compared with different membership functions.
  Keywords: Trapezoidal Neutrosophic Numbers; Neutrosophic Optimization; Accuracy Function.
  DOI: 10.1504/IJOR.2024.10064719
   
  
• Inventory Decisions for Deteriorating and Ameliorating Items with Partial Backlogging and Time Varying Demand  
  by Praveen V. P, Manoharan M 
  Abstract: The observations from real life examples indicate that the consideration of ameliorating and deteriorating items is important in developing inventory models. This research paper deals with both these factors along with demand rate which is linearly time varying over a fixed planning time frame. The shortages are partially backlogged during the time frame. Here, amelioration rate is taken to follow two parameter Weibull distribution as these items increases with time and decreases due to death. In the first phase of time horizon deterioration occurs and its rate increases with time. The model is developed under these situations. Using numerical examples and sensitivity analysis, the model is illustrated and verified the effect of changes of model parameter on the optimal solutions.
  Keywords: inventory; time dependent deterioration; Weibull distribution amelioration; time dependent demand; partial backlogging.
  DOI: 10.1504/IJOR.2024.10064792
   
  
• Distinctive representations of icosagonal fuzzy numbers along with novel defuzzification techniques for solving fuzzy assignment problems  
  by Jaya Bhadauria, Suneel Kumar, Deepak Kumar, Anita Kumari 
  Abstract: In this paper, we represent icosagonal fuzzy numbers in a variety of ways, both symmetrically and asymmetrically, linearly and nonlinearly as well as single valued and interval-valued. Along with this, we have also developed some defuzzification procedures with different approaches based on centroid method, the mean of alpha-cut method and bounded area method. In order to demonstrate the significance of these strategies, defuzzification approaches can be applied to many optimisation problems in operational research. We have applied these strategies to solve the numerical example based on fuzzy assignment problem in the interview board panel with 20 parameters. This study deals with the situation where icosagonal fuzzy numbers are used to represent ambiguous information in the form of linguistic terms. The ranking function for icosagonal number is also proposed in this paper for the better comparison of two icosagonal fuzzy numbers and its primary goal is used in this research for the appropriate allocation to resolve the fuzzy assignment problem.
  Keywords: icosagonal fuzzy number; defuzzification techniques; ranking function; fuzzy assignment problem.
  DOI: 10.1504/IJOR.2024.10065167
   
  
• Multiple Drone and Truck Combined Operations  
  by Bhawesh Sah, Mohammad Moshref-Javadi, Sung Hoon Chung 
  Abstract: Drones, also known as unmanned aerial vehicles (UAVs), have the capability to improve the responsiveness in parcel distribution process, however, drone delivery suffer from shortcomings such as limited battery life, and weight carrying constraints. These shortcomings can be overcome if a drone works in conjunction with a truck to make parcel deliveries. In contrast to earlier models which focus on single drone and truck, in this paper we propose a novel model in which multiple drones and trucks can work together to fulfill the logistics operations, a drone can depart from a truck and return back to the same or a different truck after completing the operations and a truck can house multiple drones. We present a mixed integer linear programming formulation of the problem and present results from several heuristics such as Genetic Algorithm (GA), Simulated Annealing(SA), and Greedy search combined with the Insertion method.
  Keywords: Drone Logistics; Last-mile delivery; Unmanned aerial vehicle; Transportation planning; Optimization.
  DOI: 10.1504/IJOR.2024.10065379
   
  
• A Dual Channel Supply Chain considering Different Preferences of Delivery Cost Bearing in Presence of a Third Party Platform  
  by Soumita Kundu 
  Abstract: This paper explores the operation decisions of a dual-channel supply coordination consisting of a manufacturer, a retailer and a third-party platform (3PP). In reality, in the presence of the third-party platform manufacturers can opt for various options of bearing delivery cost: 1) the manufacturer will bear the delivery cost; 2) the 3PP will bear the delivery cost; 3) the customer will bear the delivery cost. Here, we address all these situations. Furthermore, the delivery time, to which the online customers are more sensitive, is taken into consideration by customers' channel choice and the manufacturer or the third party makes efforts to reduce delivery time. Finally, sensitivity analysis with graphical illustrations has been performed to investigate whether it is profitable for the manufacturer to pay product delivery cost in the online channel, if not, under what condition the third-party platform or the online customer will bear this cost.
  Keywords: dual-channel; third-party platform; delivery time; revenue sharing; pricing.
  DOI: 10.1504/IJOR.2024.10065581
   
  
• Sperm Flagellum Motility Algorithm: a Novel Metaheuristic Optimiser for Solving Optimisation Problems  
  by Imad El Karkri, Abdelghni Lakehal 
  Abstract: The challenges and intricacies associated with real-world numerical optimisation problems have significantly increased, necessitating the development of efficient optimisation methods. While numerous metaheuristic approaches have been proposed, only a select few have gained recognition within the research community. This paper introduces a novel metaheuristic algorithm known as the sperm flagellum motility algorithm (SFMA) to address optimisation problems. SFMA emulates the motility principle of a sperm flagellum immersed in a two-dimensional fluid. Representing a population of solutions, the sperm flagellum navigates the search space to discover an optimal solution for the given problem. To evaluate the effectiveness of SFMA, a suite of 23 benchmark functions and five engineering problems were solved and compared against the performance of other widely recognised optimisers. The optimisation results demonstrate that the SFMA is highly competitive when compared to both conventional methods and state-of-the-art meta-heuristic algorithms.
  Keywords: sperm flagellum motility algorithm; SFMA; optimisation algorithm; benchmark functions; meta-heuristic algorithm; engineering problems.
  DOI: 10.1504/IJOR.2024.10065705
   
  
• Optimising Fresh Produce Demand with Expiration Date and Carbon Mitigation Strategies under Learning Effect and Non-Linear Holding Cost: A Quadratic Modelling Approach of Time-Price-Freshness for Profit Maximization  
  by Vrushali Ajitkumar Surve, Monika Naik 
  Abstract: In todays health-conscious society, there is a growing demand for fresh produce. This demand depends on factors like freshness, visibility, expiration date, and pricing, influencing customer purchases. This article suggests modelling fresh produce demand as quadratic function, incorporating freshness, price, shelf space, and expiration date under learning effects and with nonlinear holding costs. Surprisingly, it proposes maintaining significant stock towards replenishment cycle ends, challenging the zero-ending inventory norm. Simultaneously, nations are tackling carbon emissions as a major driver of global warming. Economists endorse carbon cap and tax policies. To optimise strategies (cycle time, price, and green technology), a systematic approach is proposed for profit maximisation. Numerical example validates this model, offering insight to ideal pricing, ending inventory, replenishment cycle, and backroom clearing timing, and ordered quantity. Classical optimisation techniques compute these values. Moreover, by analysing eigenvalues of a Hessian matrix, the convexity of profit function is established. A numerical illustration and sensitivity analysis on inventory parameters verify the model, yielding vital managerial insights. In essence, this article explores fresh produce demand modelling and carbon mitigation strategies while providing practical optimisation solutions and empirical validation.
  Keywords: Deterioration; Learning effect; freshness index; carbon emission; carbon tax; carbon cap; stock-time-price-freshness dependent demand.
  DOI: 10.1504/IJOR.2024.10065911
   
  
• Solving a Flexible Job-Shop Scheduling Problem considering Changes in Resources Capacity  
  by Sandra Sousa, Lopes Rui Borges 
  Abstract: This paper addresses a flexible job-shop scheduling problem (FJSP) with a set of processing characteristics commonly found in several real-world manufacturing applications. The processing characteristics considered are release dates, precedence constraints within and between different jobs, resource capacity (including downtimes, changes in capacity, and infinite capacity), and sequence-dependent setup times. The goal is to find a schedule minimising the total number of tardy jobs. To tackle the newly proposed FJSP some heuristic approaches are put forward. New constructive and improvement heuristics are proposed and some of them are used within metaheuristic frameworks, namely greedy randomised adaptive search procedure (GRASP) and iterative local search (ILS). To evaluate these approaches, two sets of test instances from the general FJSP literature have been adapted to meet the specifications of the proposed FJSP.
  Keywords: scheduling; flexible job-shop; resources capacity; heuristics; flexible job-shop scheduling problem; FJSP; iterative local search; ILS.
  DOI: 10.1504/IJOR.2024.10065977
   
  
• An Improved Chemical Reaction Optimisation for the Dynamic CVRP with Two-Dimensional Loading Constraints  
  by Nadia Dahmani, Ines Sbai, Takwa Tlili, Saoussen Krichen 
  Abstract: Efficient packing and routing are key to minimising supply chain costs and delivery times. This is particularly critical with two-dimensional items like appliances or forklifts, where maximising cargo space and optimising routes are crucial. In this study, we investigate the dynamic vehicle routing problem with two-dimensional loading constraints. This problem involves assigning routes to vehicles while considering items dimensions and minimising transport costs. To tackle this problem, we propose an improved chemical reaction optimisation algorithm. The algorithms effectiveness lies in its balance between finding known good solutions and exploring new possibilities, leading to optimal solutions. Additionally, we perform a local search scheme to refine the solution further. To address the complexity of dynamic packing, a next-fit decreasing heuristic is designed. To assess the effectiveness our algorithm, we adapt the static benchmark instances using various degrees of dynamism. Based on performance metrics, results demonstrate the competitiveness of our algorithm.
  Keywords: Dynamic vehicule routing problem; Two-dimensional loading constraints; Chemical reaction optimization; Local search; Next Fit Decreasing Heuristic.
  DOI: 10.1504/IJOR.2024.10066264
   
  
• Price Dependent Demand and Inflation Deteriorating Inventory Model with Shortages  
  by Nilesh Kumar, Quazzafi Rabbani, Nurul Khan 
  Abstract: The aim of this research is to develop a model of declining stock levels and shortages in an environment of price-dependent consumer demand and inflation. The purpose of this model is to have a conversation on how to handle an inventory system for perishable goods when demand is price-dependent on supply. Here it is believed that shortages will occur and inflation will be used to make up for some of the shortfall. As a result, the economic order quantity is calculated to maximise profits per unit of time.
  Keywords: deteriorating; shortages; inventory model; price dependent demand; inflation.
  DOI: 10.1504/IJOR.2024.10066329
   
  
• A fuzzy medication inventory model with Weibull deterioration and shortages for any epidemic or medical emergency  
  by Swadhin Banerjee, Tripti Chakrabarti 
  Abstract: As medication costs are a large portion of operating expenses of any hospital, improving the medication inventory management is a great way to decrease overall healthcare cost. Again, in recent COVID-19 pandemic, healthcare sector faced many challenges, especially uncertain increase in the demand and shortages of supply of medical items. Thus, this study aims to derive an optimal medication inventory model to manage any future epidemic or medical emergency as well as to reduce overall healthcare costs. We would use Gaussian fuzzy number which is more realistic to counter uncertainties of epidemic or medical emergency. Also, we would present a numerical illustration with sensitivity analysis to exemplify our proposed model.
  Keywords: EOQ model; deterioration; time dependent demand; shortages; fuzzy environment.
  DOI: 10.1504/IJOR.2024.10066450
   
  
• Performance Analysis of M/M/1/N Queue with Balking and Reneging using Statistical Mechanics  
  by Krishan , Neetu Gupta, Amita Bhagat 
  Abstract: Customer's behaviour is the most important parameter in deciding the effictiveness of a queueing system. Sometimes out of impatience the customers get discouraged and behave differently on analysing the queue. In this article, statistical methods are used to analyse finite markovian M/M/1/N queueing system with balking and reneging. The performance measures and cost model are constructed and numerical examples are illustrated to analyse the sensitivity of the model.
  Keywords: Balking; Reneging; Performance Measures; Steady-State Probabilities; Cost Model.
  DOI: 10.1504/IJOR.2024.10066698
   
  
• Exploring Metaheuristic Strategies and Risk Mitigation Techniques for Diversified Portfolio Management  
  by Jhuma Ray, Soma Panja 
  Abstract: Recent portfolio selection models pose considerable challenges for optimization due to the introduction of nonlinearities, discontinuities, and high-dimensional efficient frontiers. Exact techniques need to address these complexities effectively. Consequently, researchers here have turned to metaheuristics as robust tools capable of finding near-optimal solutions, or even the global optimum, within reasonable computational time. Metaheuristics, including Genetic Algorithms (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO), along with Ant Colony Optimization (ACO), have demonstrated their efficacy in handling the intricacies of complex portfolio optimization problems as reported here. Following the examination of four meta-heuristic methods, consensus results are derived. Subsequently, a Friedman test is employed to determine the method's superiority. Differential Evolution along with Particle Swarm Optimization emerges as the superior choice among the four proposed metaheuristic methods, with the other two as points of comparison emphasizing single-objective metaheuristics (SOMHs) offering natural framework for addressing portfolio selection challenges involving intricate risk measures and diverse real-world constraints.
  Keywords: Genetic Algorithm; Differential Evolution; Particle Swarm Optimization; Ant Colony Optimization; Conditional Value at Risk; Value at Risk.
  DOI: 10.1504/IJOR.2024.10066702
   
  
• Continuous Review Inventory Model under Partial Permissible Delay in Payment Policy and Capacity Constraint  
  by M. A. Darwish  
  Abstract: In this paper, we propose a continuous review inventory model incorporating the following real-world aspects; randomness in lead time, partial permissible delay in payment, and capacity constraint on retailer's warehouse. Given the current disruptions in lead times due to epidemics and wars, the deterministic lead time assumption is probably not valid. We, thus, relax this assumption to match the real-world situations. We model the lead time randomness by a general probability distribution. Moreover, we use the partial permissible delay in payment policy where the retailer pays a down payment at the time of placing the order and the outstanding amount is settled by the time of placing the next order. Further, we relax the infinite capacity assumption by imposing a constraint on the lot size. The model determines the optimal lot size and reorder point that minimize the net expected total cost per time unit.
  Keywords: Inventory; stochastic lead time; partial permissible delay in payment policy; capacity constraint.
  DOI: 10.1504/IJOR.2024.10066917
   
  
• Analysis of AS/RS Performance for Different Storage Location Policies  
  by Aravindh Kumaran L, Pradeep Arul Pragash A 
  Abstract: Automated storage and retrieval systems (AS/RS) play a significant role in storing and retrieving inventory in warehouses and manufacturing plants. These systems rely on storage location policies to guide the effective storage and retrieval of inventory. These storage location policies are in alignment with the inventory handling systems including AS/RS. This paper aims to analyse the impact of different storage location policies on the runtime and performance of AS/RS. Data was collected from a chocolate manufacturing company's warehouse, and a simulation of an AS/RS model with a single storage rack was conducted to analyse the performance with different settings. The results showed that the first available storage policy performed well for a setting called no batch dispatches. The findings contribute to a better understanding of the factors influencing the performance of AS/RS and can assist in optimising inventory handling processes in warehouses and manufacturing plants.
  Keywords: automated storage and retrieval systems; AS/RS; storage location policies; warehouse; simulation; inventory.
  DOI: 10.1504/IJOR.2024.10066958
   
  
• A Sustainable Deteriorating Inventory Model under Controllable Carbon Emission with Green Investment Strategy and Quality Reliant Demand  
  by Sandeep Kumar, Sanjai Sharma, Anirudh Bhargava, Dharmendra Yadav 
  Abstract: In this era of technological progress and its applications, which offer enhanced survival facilities, on one side, the use of technology reduces the burden of work, and on the other, it has become the main cause of carbon emissions. The present work focuses on the reduction of carbon emissions and the development of an inventory model that maximises the retailer’s profit. In this work, a model incorporating a quality-reliant demand with shortages and an expiration rate of deterioration is developed. Energy-efficient green technology is adopted to make in a greener environment. The model was developed with the objective of determining the time period at which inventory vanishes and the value of the scale parameter, which determines a reasonable selling price that is a multiple of the unit purchase cost so as to maximise profit. To elucidate the model’s applicability, different numerical examples have been illustrated.
  Keywords: shortages; quality dependent demand; single item; green investment; quality dependent holding cost; expiration date reliant deterioration.
  DOI: 10.1504/IJOR.2024.10067082
   
  
• Economic Analysis of MAP/PH/1 Queueing Model with Degrading Service Rates, Multiple Vacations, and Reactive Maintenance based on Feedback  
  by Nitin Mahla, Alka Choudhary, Dinesh Chandra Sharma 
  Abstract: The proposed model introduces a queueing model with a Markovian arrival process and Phase type service distribution where the server undergoes degradation over time, leading to a reduction in service rates, and customer feedbacks are collected to evaluate customer satisfaction. Reactive maintenance is implemented on the server after the accumulation of a predetermined quantity of adverse feedback occurrences. Vacation policies are implemented to restore the server to its optimal service rate either when a degradation threshold is reached or when there are no customers in the system. Steady-state probabilities and performance metrics are derived using the Matrix geometric method. Economic functions are developed and the Particle Swarm Optimisation (PSO) technique is used to optimize the predicted profit. The effects of system parameters are summarised in the numerical example section.
  Keywords: Markovian arrival process; Phase type distribution; Feedback; Reactive maintenance; Service rate degradation; Multiple vacation.
  DOI: 10.1504/IJOR.2024.10067165
   
  
• A Multi-Objective Optimisation of Airport Gate Assignments integrating Economic, Environmental, and Social Sustainability  
  by Jeremy Gabriel Uy, Jarvy Larz San Juan, Mateo Urera, Edrik Aaron Chan, Ezra Emmanuel Cruz, Nathaniel Harvey Tang, Richard Li, Jayne Lois San Juan, Charlle Sy 
  Abstract: With the increasing trend in the number of passengers in air travel, especially after the pandemic. As such, researchers have been attempting to optimise airport systems to stop problems from arising. While optimisation studies in AGA systems have been looked into with the economic, environmental, and social perspective separately, studies yet to consider integrating the three considerations and optimising the problem simultaneously, which would allow multiple parties to benefit at the same time minimising the compromission made in each objective individually. A multi-objective mathematical optimisation model has been developed for AGA optimisation considering economic, environmental, and social objectives simultaneously. An illustrative case study was executed to understand the tendency of the model. The model has been validated to be effective in minimising total turnaround costs, minimising total walking distance, and minimising carbon emissions simultaneously.
  Keywords: airport management system; triple bottom line; green transportation; aircraft gate assignment; air transport.
  DOI: 10.1504/IJOR.2024.10067262
   
  
• Hybrid Algorithm: Estimation of Distribution Algorithm and Local Search Method for the 0-1 Quadratic Knapsack Problem  
  by Amina Arousse, Bassem Jarboui, Mohamed Cheikh 
  Abstract: In this paper we propose a hybrid algorithm based on an estimation of distribution algorithm (EDA) and the local search method (LS) to solve the problem of the quadratic knapsack. It is important to note that the estimation of distribution algorithm and the local search method are often less powerful on certain types of problems. It does not guarantee efficient results in a finite time. To solve this deficiency, we suggest this hybridization. The proposed algorithm was tested over a set of small-sized instances with 100 and 200 binary variables and the results were compared to those reported from the literature.
  Keywords: Estimation of Distribution Algorithm; Local Search Method; 0–1 quadratic knapsack problem.
  DOI: 10.1504/IJOR.2024.10067264
   
  
• Safety Stock Optimisation with Guaranteed Service Approach under Different Supply Chain Topologies  
  by Joaquim Jorge Vicente  
  Abstract: Supply chain structure is an important driver to estimate de safety stock level in each entity. Supply chain inventory planning is studied under uncertain demand. The present paper uses guaranteed service approach to optimise safety stocks. The problem is formulated as a mixed integer linear program (MILP) that consists of determining the optimal locations and quantities of safety stock in the network to meet a given service level. The model is applied to different supply chain structures to study the impact on safety stock optimisation. Also, it determines inventory levels in each entity and reordering plan for the operating network that minimises the overall operation cost. A case study with two supply chain structures is presented and solved.
  Keywords: supply chain management; safety stock; optimisation; mixed integer linear programming; MILP; guaranteed service approach; supply chain topology.
  DOI: 10.1504/IJOR.2024.10067644
   
  
• Sustainable Freight Transportation: a Bi-Objective Multi-Choice Goal Programming Framework  
  by Nitin Sahu, Garima Mittal 
  Abstract: Sustainability has gained significant prominence in transportation policies and planning. The surge in air pollution, primarily attributed to greenhouse gas emissions from industrial transport, has impacted both developed and developing nations, including India, prompting the pursuit of sustainable transportation solutions. This paper presents an integrated, multi-objective, multi-choice optimisation model to address the challenging problem of sustainable freight transport in the industrial sector. The model considers real-world constraints to lower transportation costs and greenhouse gas emissions. We employ a multichoice goal programming technique to develop optimal allocation strategies. We recognize that decision-makers use interval values to designate multiple aspiration levels for transportation cost and greenhouse gas emissions objectives. To demonstrate the practical application of the proposed optimisation model and the solution methodology, we present a numerical illustration in the context of the transportation of municipal solid waste to an energy generation plant.
  Keywords: Multi-choice goal programming; transportation problem; Sustainable transportation; Greenhouse gas emissions.
  DOI: 10.1504/IJOR.2024.10067865
   
  
• An Innovative Partial Backlog Supply Model that Incorporates the Time-Sensitive Degradation, Influenced by the Time-Linear Cost of Holding and Selling Price-Dependent Split Demand  
  by Vanjikkodi Thirumalaisamy, Pankajam V 
  Abstract: An innovative inventory model involving price-dependent split demand is developed in this article. The objective is to maximize the total revenue from an inventory system which operates on a split demand, time-linked deterioration, linearly varying holding cost, time-dependent shortage, and partial backlogging. This model is a comprehensive tool to enhance inventory management practices and optimise profitability, employing a computational algorithm supported by proven results to achieve maximal profit. This holistic approach to decision-making in inventory management is illustrated through a numerical example and sensitivity analysis, demonstrating how system limits influence optimization outcomes and assess concavity. The Sensitivity analysis explores parameter variations affecting economic order quantity (EOQ), ideal ordering time, overall profit, etc., with MATLAB facilitating graphical visualization of model parameter relationships.
  Keywords: EOQ; Price and time-dependent split demand; shortage; time-varying deterioration; linear holding cost; partial backlogging.
  DOI: 10.1504/IJOR.2024.10068050
   
  
• Analysis of Batch Arrival General Service Queue with Feedback, Balking, General Delay Time and Second Optional Service  
  by Vijaya Laxmi Pikkala, Hasan Qrewi, Andwilile Abrahamu George 
  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: Batch arrival; Non-Markovian; Feedback; Balking; First essential service; Second optional service; Breakdown; Queue.
  DOI: 10.1504/IJOR.2023.10068061
   
  
• Two and Three Rates of Production Inventory Models for Deteriorating items with Price-Advertisement Dependent Demand  
  by Sivashankari C.K., Dr. Choudri V. 
  Abstract: The paper presents two and three rates of production inventory models for deteriorating items with price-advertisement-dependent demand. It considers a pre-determined price and the possibility of switching production rates later. This latter scenario is advantageous because it reduces inventory costs by avoiding the need to initially produce a large quantity of items. Every inventory model starts with the premise that the demand rate is static and never changes. But in reality, the demand rate does fluctuate. The three-rate production inventory model for depreciating goods whose demand is based on price ads is examined first, followed by the two-rate model for depreciating goods whose demand is dependent on price advertising. In this research, we compare and contrast these two models. Both approaches validate the rule of demand and find the price at which a business breaks even. The ideal manufacturing lot size that minimises overall cost is found by developing an appropriate mathematical model. Numerical verification and sensitivity analysis are included, and illustrative examples are given. This models validation code was written in Visual Basic 6.0.
  Keywords: Two rates of production; Three rates of production; Deteriorating items; Price-advertisement demand; Price-breakeven point; Law of demand.
  
  
• An Inventory Model for Deteriorating Items that are about to Expire with a Variable Rate of Deterioration, Stock and Demand that is Influenced by selling Price under Inflation  
  by A. Lakshmana Rao, Achyuta S, Arun Kumar Saripalli, Suryanarayana P. S. Kornu 
  Abstract: Achieving sustainable development goals faces numerous challenges, particularly in response to rapid environmental changes and evolving client preferences. Demand for goods is closely tied to inventory levels, yet excessive stock increases holding costs and environmental impact. Timely sales before product expiration are crucial to mitigating environmental harm. Any model must account for deterioration and inflation due to their significant impact. The proposed model manages expiring items by considering stock levels, selling prices, and demand influenced by product lifespan, inflation, and variable deterioration. It integrates expiration dates and the weakening effect of green products to maximise benefits in an inflationary setting. Mathematical analysis identifies the optimal yield to enhance profitability. Sensitivity analysis, supported by numerical examples, further validates the model's practicality.
  Keywords: Selling price; Stock; Expiring items; Inflation; deteriorating items; lifespan and Lifetime dependent demand.
  DOI: 10.1504/IJOR.2024.10068473
   
  
• Continuous-to-Combinatorial Mapping Framework to Solve Combinatorial Problems  
  by Karim Bechiri, Abdesslem Layeb 
  Abstract: Combinatorial Optimization Problems (COPs) are fundamental in operations research, addressing complex challenges in many fields. Traditional exact methods guarantee optimal solutions but become computationally infeasible for large instances, necessitating the use of heuristics and metaheuristics. This paper introduces a Continuous-to-Combinatorial Mapping (CCM) framework designed to bridge continuous metaheuristics with combinatorial problems, converting continuous solution vectors into valid permutations. Using the TSP as a case study and employing the Tangent Search Algorithm (TSA) as the optimizer, the experiments conducted on well-known TSP instances reveal that the CCM, especially with the new Weighted Relative Permutation Tree (WRPT) and Weighted Absolute Permutation Tree (WAPT) mapping functions, achieves rapid convergence to high-quality solutions. These findings suggest that the CCM framework is an efficient and adaptable tool for tackling combinatorial problems, exploiting the capabilities of continuous metaheuristics.
  Keywords: Combinatorial Optimization; Metaheuristic; Continuous-to-Combinatorial Mapping; Traveling Salesman Problem.
  DOI: 10.1504/IJOR.2024.10068589
   
  
• Steady state analysis of M/G(1,b)/1 queue with multiple working vacation  
  by Gulab Patel, Gagan Kumar Tamrakar 
  Abstract: This study investigates an infinite buffer batch size dependent (BSD) bulk service queue (BSQ) with multiple working vacations (MWV). Customers enter the system according to a Poisson process, where the server attends to the customers in batches during the regular period (RP). Once the system becomes empty, the server takes a vacation, following the exponential vacation time distribution. During the vacation, the server serves a single customer (if any) with a reduced rate than the service rate of a single customer in the RP. Service time in RP and the working vacation period (WVP) follow the general distribution. Using the supplementary variable technique (SVT) and bivariate generating function approach, the joint probabilities of the queue content and server content are obtained at arbitrary epochs during RP. Also, we derive the queue length probabilities at arbitrary epochs during WVP. Several performance metrics are presented. Numerical computation has been performed to validate the analytical results.
  Keywords: Batch size dependent service; GBS rule; Infinite buffer; Multiple working vacation; Bivariate generating function.
  DOI: 10.1504/IJOR.2025.10069432
   
  
• Using Convolution Kernels to Improve Ant Colony Optimisation  
  by Emilio Singh 
  Abstract: Ant algorithms are a popular class of meta-heuristic optimisation algorithms primarily used to solve combinatorial optimisation problems. Much research has been done into trying to improve ant algorithms. However, existing research has largely focused on improving the algorithms with techniques and ideas from the realm of meta-heuristics. This paper presents the kernelised ant colony optimisation algorithm which uses an adapted convolution kernel from the domain of image processing. This paper presents a comparative study of the kernelised ant colony optimisation algorithm against popular ant algorithms. The results showed that, in three domains (movie scene scheduling, travelling salesperson and search location assignment problem domains), the algorithm outperformed the ant algorithms in producing the best quality solutions by statistically significant margins. The paper provides theoretical and empirical evidence for the performance improvements, by demonstrating how convolution kernels can be adapted for ant algorithms, and how that adaptation results in improved performance.
  Keywords: Ant Colony Optimisation; Combinatorial Optimisation; Convolution Kernel.
  DOI: 10.1504/IJOR.2025.10069521
   
  
• Evaluating ER Crowding Metrics: a Criteria-Based Approach  
  by Adam DeHollander, Mark Karwan, Sabrina Casucci 
  Abstract: This paper introduces a novel framework for evaluating emergency room (ER) crowding metrics using standardised criteria, addressing a gap in current methodologies. ER crowding is a critical issue, and inconsistent evaluation of metrics complicates decision-making. Our framework includes three key criteria: 1) false jumps, assessing stability and reliability; 2) entropy, measuring information richness and adaptability; 3) sensitivity to atypical events, evaluating responsiveness to deviations. We apply this framework to four widely used metrics EDWIN, NEDOCS, weighted length of stay (LOS), and weighted occupancy (WO) to demonstrate its utility. While EDWIN performs robustly, our studys focus is not ranking metrics but validating a structured approach for their assessment. This framework provides a consistent, objective method for evaluating ER crowding metrics, supporting improved decision-making in healthcare settings.
  Keywords: ER crowding metrics; criteria-based evaluation; metric stability; entropy; healthcare systems analysis; emergency department overcrowding; false jumps; metric sensitivity; EDWIN; NEDOCS.
  DOI: 10.1504/IJOR.2025.10069601
   
  
• Modelling Lifetime Data and Athletes Red Cells Counts Using The New Topp-Leone-Lomax Poisson Distribution  
  by Fastel Chipepa, Mavis Pararai, Zvivko Nedev 
  Abstract: A novel distribution developed via compounding of the Topp-Leone-Lomax and the Poisson power series distributions is developed. The new distribution is an infinite mixture of the exponentiated Topp-Leone-Lomax distribution. This tractability property is highly desirable for new generalised distributions as it aids in deriving the other statistical properties of the developed distribution. The authors derived some statistical properties of this model, which include quantile function, moments and moment generating function, distribution of order statistics and R
  Keywords: Topp-Leone Lomax Distribution; Power Series Distribution; Maximum Likelihood Estimation; Poisson Distribution.
  DOI: 10.1504/IJOR.2025.10069671
   
  
• Metaheuristic Approaches for Quadratic Assignment Problem: Recent Advancements and Potential Path  
  by Syariza Abdul-Rahman, Nurdiyana Jamil, Aida Mauziah Benjamin, Sharifah Shuthairah Syed-Abdullah, Ku Ruhana Ku Mahamud, Mohamed Saifullah Hussin 
  Abstract: Quadratic Assignment Problem (QAP) is a crucial optimization problem due to its applicability in real-life applications. The QAP is an NP - hard and impossible to be solved in polynomial time for large instances. This makes heuristic and metaheuristic approaches good solution quality for short computation time, instead of exact approaches. Metaheuristics are among the most effective and widely used approaches for solving the QAP. Therefore, it is integral to comprehend the characteristics of the potential approaches to cope with the complexity of QAP. This paper presents an extensive literature review regarding the various recent advancements of metaheuristic approaches for QAP. Subsequently, problem formulation and discussion on the recent advancements and potential path of the approaches for QAP are highlighted. This review can enhance one's comprehension towards the metaheuristic approaches for QAP that serve as solutions and initiate potential improvement for a more effective approach.
  Keywords: Quadratic Assignment Problem; Heuristics; Metaheuristics; Single Search-based; Population Search-based.
  DOI: 10.1504/IJOR.2024.10069674
   
  
• Conflict Resolution in an Air Traffic Sector via Delayed Aircraft Entrance  
  by Damir Hasic, Adis Alihodzic, Elmedin Selmanovic, Admir Besirevic, Sead Delalic 
  Abstract: One of the fundamental principles in air traffic management is collision detection and avoidance. It almost always consists of changing an aircraft's direction and speed. These changes increase air traffic controllers' workload. We present a model for collision avoidance while maintaining aircraft headings and speed unchanged by postponing aircraft entrance into a sector. This approach helps reduce controllers' workload while increasing air traffic smoothness. The workload is delegated to neighbouring sectors, which can benefit complex air sectors adjacent to air sectors of lesser complexity. The model is a 3D, continuous-time model that directly minimizes time delay. We transform it into an equivalent model with linear, logical constraints and solve it using CPLEX.
  Keywords: OR in airlines; Nonlinear programming; Air traffic management; Conflict avoidance; Global optimization.
  DOI: 10.1504/IJOR.2025.10069779
   
  
• An Integrated Economic Model of High Nutritional Crop Millet  
  by Sushree Swetapadma, Mitali Madhumita Acharya, Srikumar Acharya 
  Abstract: This study has proposed a mathematical framework to analyse production and profit margins for millet farmers. To optimize the utility of this crop, a linear programming (LP) problem is initiated. The proposed LP model provides quantitative analysis as well as an analytical survey of millet production, which results growth of minimum support price. The main purpose of the model is to improve profit acquired by millet farmers and support in distress sales to procure food grains for public distribution. In the proposed model, different types of nutrient counts of millet crop have taken for observation, which provides major health benefit and secured source of livelihood. Karmarkar's interior point LP algorithm with advanced steps has been implemented to solve the model as it works more efficiently on large scale problems. Real data from the Indian Institute of Millets Research (IIMR) has been taken in order to justify the model.
  Keywords: Linear programming problems; Mathematical programming; Interior point method; Karmarkar's projective scaling algorithm; Production planning; Agriculture management; Minimum support price.
  DOI: 10.1504/IJOR.2025.10069783
   
  
• Distributional Censoring and Optimal Service Contracts  
  by Kiefer Joe Burgess, Stanko Dimitrov, Qi-Ming He 
  Abstract: We consider a customer service scenario wherein an employer seeks to hire an employee to complete a customer service request. After establishing the game dynamics and solving for the optimal set of contracts, we consider howthese optimal contracts change under various changes in operating conditions. Our main contribution is in modelling changes in business operating rules or procedures, which we call Operational Protocol Modifications (OPMs), as instances of distributional censoring on the distributions of service quality and service time. We round out our analysis by modelling training and knowledge enhancement initiatives as first-order stochastic dominant shifts in the distributions of service quality and service time.
  Keywords: mechanism design; operations management; distributional censoring; operational protocol modifications; game theory; contract theory; service systems; stochastic modeling; operations research.
  DOI: 10.1504/IJOR.2025.10069825
   
  
• Enhancing Supply Chain Finance through Blockchain Technology: Drivers and Implementation Strategies  
  by Jiayang Xie, Yasanur Kayikci, Syed Imran Zaman, Fatima Tul Zehra, Sharfuddin Khan 
  Abstract: Blockchain technology (BCT) has emerged as a transformative tool in supply chain finance (SCF), significantly enhancing transparency and efficiency. This study, informed by a thorough literature review and consultations with academic and industry experts, identifies and validates the critical factors necessary for seamless BCT integration in SCF, with a particular focus on Pakistan's banking sector. Using the Fuzzy-DEMATEL method, the study reveals peer-to-peer transactions as primary drivers for BCT adoption in SCF. Additionally, it emphasizes the importance of risk reduction, digitalization levels, and factors related to trust, collaboration, and quality assurance. Based on these findings, it is strongly recommended that the State Bank, in collaboration with the Securities and Exchange Commission, develop and implement regulatory frameworks to promote BCT adoption. Establishing comprehensive data acquisition protocols and clear implementation guidelines will be vital to successfully introducing blockchain features across the country and fostering a robust and transparent financial environment.
  Keywords: Blockchain technology; Supply chain finance; Peer-to-peer transaction; Banking sector; Fuzzy-DEMATEL.
  DOI: 10.1504/IJOR.2025.10069828
   
  
• Cost Investigations and Performance Modelling of a State-Dependent M/M/1/K Queue with Working Vacation and F-Policy  
  by Sudeep Singh Sanga, Nidhi Nidhi, Ayushi Gupta 
  Abstract: This investigation deals with a state-dependent M/M/1/K queueing model with working vacation and F-policy. The admission control F-policy focuses on effectively managing the arrival of customers in the system. In situations where the working vacation concept is integrated into the system, the server continues to serve customers during a vacation period instead of completely stopping. The model is mathematically formulated and then solved using the matrix-analytic method, which helps us to calculate steady-state probability distributions and derive various key performance measures. Numerical analysis of the performance measures is conducted to evaluate the system characteristics under different parameters, revealing insights into its system behaviour. The cost function is introduced to capture the economic aspects of the system in terms of average queue length, busy server probability, and system utilisation. Then, we optimize the cost function using the quasi-Newton method (QNM) and particle swarm optimization (PSO). These techniques provide distinct methodologies for determining the optimal service rates at the minimum cost, and their outcomes are compared. The application of the proposed study is exemplified in cloud computing.
  Keywords: State-dependent queue; F-policy; Working vacation; Cost; QNM; PSO.
  DOI: 10.1504/IJOR.2025.10070010
   
  
• The Quadripartitioned Neutrosophic Soft Matrix Theory Provides a Comprehensive Framework for Addressing the Uncertainties and Complexities Inherent in Disaster Management  
  by Ramesh Kumar S, Stanis Arul Mary A 
  Abstract: The objective of this paper is to broaden the Quadripartitioned Neutrosophic Soft Matrix (QNSM) theory by integrating essential definitions from classical matrix theory into the theoretical framework of quadripartitioned neutrosophic soft sets. Supported by relevant examples, the manuscript introduces a decision-making theory that incorporates a dedicated solution algorithm. The proposed theory is demonstrated through case studies. The manuscript tackles inherent challenges in decision-making problems by presenting efficient methodologies to navigate and resolve them. It investigates various decision-making scenarios, thoroughly examining existing theories and frameworks. The paper introduces innovative approaches, exemplified by specific methods or algorithms, to improve the efficiency of decision-making processes in the field of disaster management. Combining theoretical analyses and practical examples, the paper demonstrates the applicability and advantages of these proposed methodologies. The findings contribute significantly to the ongoing discussion on decision-making across different contexts, providing valuable insights for researchers and practitioners alike.
  Keywords: Quadripartitioned Neutrosophic Soft Set (QNSS); Quadripartitioned Neutrosophic Soft Matrix (QNSM),.
  DOI: 10.1504/IJOR.2025.10070011
   
  
• A Single Server Markovian Queueing Model with Encouraged Arrival and Threshold Dependent Impatience  
  by Amit Choudhury, Pallabi Medhi, Dolismita Boruah 
  Abstract: We present the stochastic modelling of a single server infinite capacity Markovian queue with encouraged arrival, threshold limit, balking, and position-dependent reneging. There are various real-life queuing systems that incorporate all these disparate features. Our modelling is novel because the incorporation of all these features together has not been attempted in literature. Markov processes and probability generating functions are employed to derive the steady-state probabilities and closed-form expressions of various performance measures. In addition to traditional performance measures, some newly devised performance measures are presented, which facilitate the assessment of the system's efficacy. A comprehensive sensitivity analysis is conducted numerically to evaluate the variation in performance measures with variations in the system parameters. Our analysis examines the queueing model's resilience, offering nuanced insights into its behaviour under different conditions. A numerical example is presented that illustrates the analytical outcomes, confirming the model's efficacy and versatility in practical scenarios.
  Keywords: Markovian; threshold limit; encouraged arrival; reneging; balking; impatience.
  DOI: 10.1504/IJOR.2025.10070083
   
  
• Price-Advertisement-Sales Return Dependent Demand for Imperfect Production system with Prime and Challange Shortages  
  by Sivashankari C.K., Dr. Choudri V. 
  Abstract: This paper examines an imperfect production system characterised by demand dependent on price, advertisement, and sales returns, with considerations for both prime and challenge shortages modelled using a third-order equation. The study focuses on three key decision variables: optimal pricing, production lot size, and shortages. To the best of my knowledge, no previous research has developed models addressing prime and challenge shortages while incorporating demand dependence on price, advertisement, and sales returns within a third-order equation. The breakeven price is analysed, and the law of demand is verified. The objective is to determine the optimal price, production lot size, and shortage levels to maximise overall profitability in an imperfect production system. A mathematical model is proposed to evaluate the best values for the three decision variables. The models validity is demonstrated through a numerical experiment, and sensitivity analysis is conducted to assess the impact of inventory factors on profitability and provide managerial insights. The results were validated using a program developed in MS Visual Basic 6.0.
  Keywords: Price-advertisement-sales return demand; Prime and Challange shortages; Optimal priceing; Optimal production lot size; optimal shortages; price break even point.
  DOI: 10.1504/IJOR.2025.10070168
   
  
• Trade-in Strategies with Brand Differences and Reference Rebates  
  by Yan Wang, Ying Dai, Zujun Ma 
  Abstract: In a duopoly market with brand differences, both enterprises can offer within-brand or cross-brand trade-in programs. Based on the Hotelling model, the purchasing behaviour of three kinds of consumers is analysed under two decision-making sequences. The optimal decision-making sequence, pricing, trade-in strategies, rebates, and the impact of the brand difference and product durability on profits are analysed. The results show that product prices remain constant under each decision sequence. The competitor's trade-in strategy does not influence the rebates and market share. The rebate and rebate correlation coefficients set by the dominant brand are consistently higher than those set by the inferior brand. Both enterprises realise higher profits when adopting the cross-brand trade-in strategy and strategizing after their competitors have made decisions. When both repeat consumer markets are fully covered, the trade-in rebates offered by both enterprises are equivalent, resulting in the highest profitability.
  Keywords: trade-in; brand differences; reference rebate; Hotelling model; competition; duopoly market; static game; optimal solution; pricing; decision sequence.
  DOI: 10.1504/IJOR.2025.10070174
   
  
• Optimization of Neutrosophic Inventory Model with Stock and Price Dependent Demand and Weibull Deterioration for Epidemic Management using Genetic Algorithm  
  by Swadhin Banerjee, Tripti Chakrabarti 
  Abstract: During any epidemic or medical emergency generally patient parties rush to the hospitals where availability of treatment is very high and price is reasonable. To meet this requirement, hospitals need to manage their inventory accordingly for achieving satisfactory service-level output and optimal profit. Consequently, this study derives an EOQ (Economic Order Quantity) model having stock and price dependent demand and two parameter Weibull deterioration for medication inventory. Input costs are to be considered as Single Valued Gaussian Neutrosophic Numbers, as costs are usually uncertain during epidemic. Finally, we obtain numerical result by using Genetic Algorithm library PyGAD of Python programming language.
  Keywords: EOQ model; Weibull deterioration; Neutrosophic environment; Shortages; Full backlogging; Genetic algorithm.
  DOI: 10.1504/IJOR.2025.10070275
   
  
• Availability Prediction of K-out-of-N: G Fault Tolerant System with Unreliable Server  
  by Madhu Jain, Rakesh Kumar Meena, Vijay Pratap Singh, Mayank Singh 
  Abstract: This investigation deals with the performance prediction of a fault tolerant system (FTS) having standby support (SS), imperfect coverage and imperfect repair. A finite capacity M/G/1 queueing model is developed and the supplementary variable technique (SVT) is utilized to derive the queue size distribution of failed units. To examine the performability and availability of the system, the reliability metrics including availability of the FTS have been established for three particular service time distributions viz. exponential (Exp), 3-stage Erlang (E3), and deterministic (D). These indices have been computed numerically to look at the real time performance of the system under specific configurations. The system’s cost model developed and optimized using meta-heuristic optimization techniques viz. particle swarm optimization (PSO) and genetic algorithm (GA). In addition, the numerical outcomes for the availability indices acquired by the analytical formulae are compared with the outcomes obtained through the adaptive neuro fuzzy inference system (ANFIS).
  Keywords: K-out-of-N: G; M/G/1 queue; Fault tolerance; Server failure; Availability; Particle swarm optimization; Genetic algorithm.
  DOI: 10.1504/IJOR.2025.10070290
   
  
• Introduction Timing for Successive Generational Products: Insights and Analysis based on Demand-Cost Trade Off  
  by Hitesh Kumar, Mohini Agarwal, Adarsh Anand, Rajiv Chopra, Ompal Singh 
  Abstract: The objective of this paper is to analyse cost functions and determine the optimal launch time for new product generations. In today's competitive environment, where rapid technological advancements challenge new innovations, maintaining a strong market position is essential for success. Companies continually improve products to maintain dominance, often by introducing next-generation offerings. However, the timing of such introductions is a significant dilemma. Launching too early can hinder the recoupment of development costs, while delaying the launch risks missing profit-maximising opportunities. This paper examines various product sales scenarios and associated costs, emphasizing the complexity of the decision-making process. Through illustrative examples, the authors highlight the importance of choosing the right time for product launches by analysing different scenarios and costs, using statistical analysis tools to provide valuable insights. The findings offer actionable guidance for companies navigating this critical decision-making challenge.
  Keywords: Innovation; Innovation Diffusion Process; Optimal Introduction Time; Successive Generation.
  DOI: 10.1504/IJOR.2025.10070337
   
  
• Harnessing fuzzy rhotrices for accurate medical diagnosis through symptom disease rhotrices  
  by B. Ananda Priya , P. Gnanachandra  
  Abstract: A new mathematical construct, fuzzy rhotrices, is proposed as an extension of traditional matrices within fuzzy set theory. The study establishes a foundational framework for fuzzy rhotrices, defining essential operations and investigating their fundamental properties. A norm for fuzzy rhotrices is introduced and its properties are analyzed. Moreover, the research explores the practical application of fuzzy rhotrices in the field of medical diagnostics, particularly in enhancing decision-making processes. The study argues that fuzzy rhotrices offer a powerful mathematical framework capable of accommodating the inherent vagueness and ambiguity present in patient symptoms, test results, and diagnostic criteria. The findings of this study suggest that fuzzy rhotrices hold significant promise as a versatile and powerful tool in both theoretical and applied contexts, particularly in situations where traditional mathematical approaches may not be sufficient to handle uncertainty and imprecision.
  Keywords: fuzzy rhotrix; rhotrix; heart-based fuzzy multiplication; decision making.
  DOI: 10.1504/IJOR.2025.10070627
   
  
• Retailer’s inventory model considering time-dependent holding cost and temperature-reliant deterioration with preservation technology  
  by Ekta Aggarwal, N.K. Gaur, Dharmendra Yadav 
  Abstract: The rise in temperature presents a big challenge for retail industries especially dealing in food products, bakery products, etc. To address this issue, a retailer-focused economic order quantity model taking temperature-sensitive deterioration rate into account is developed. The model takes preservation mechanism into account to lessen the impact of temperature on the deterioration process. The model takes time-dependent holding cost into account. The model includes ordered quantity-dependent transportation charges. Finding the optimum cycle time, selling price, and advertising frequency to optimise the system's profit is the target of the current study. Here, an optimisation method based on calculus is used to determine the optimum solution. The model is validated using numerical analysis. The outcome shows that choosing appropriate preservation technology can lower the rate of deterioration and increase system profit. Finally, managerial insights derived from sensitivity analysis are presented.
  Keywords: EOQ model; preservation technology; ordering cost reduction; time dependent holding cost; multi-variate demand; temperature-sensitive deterioration rate.
  DOI: 10.1504/IJOR.2025.10070681
   
  
• Project Portfolio Selection using Multi-Choice Goal Programming Approach  
  by Rahul Kumar, Garima Mittal 
  Abstract: Project portfolio selection is one of the most critical issues strategists face today due to a large set of criteria and several constraints for the selection of projects. Project portfolio selection problems are inherently multi-objective in nature, with profit maximisation and risk minimisation among the most critical objectives. In this paper, along with the classical multi-objective approach, we have used multiple aspiration levels for portfolio risk and portfolio value objectives. Realistically, these aspiration levels align directly with the organisation's strategy and risk profile. The novelty of the proposed work lies in the use of the multi-choice approach to map the risk-return aptitude of the decision-maker within the multi-objective framework of portfolio selection. This approach helps construct a project portfolio based on multiple aspiration levels of the decision-maker.
  Keywords: Project portfolio selection; Multi-choice; Goal programming; Multiple aspiration levels; Project risk; Net present value; Multi-objective.
  DOI: 10.1504/IJOR.2025.10070917