Template-Type: ReDIF-Article 1.0 Author-Name: T. Deepa Author-X-Name-First: T. Author-X-Name-Last: Deepa Author-Name: A. Azhagappan Author-X-Name-First: A. Author-X-Name-Last: Azhagappan Title: Analysis of state dependent M[X]/G(a, b)/1 queue with multiple vacation second optional service and optional re-service Abstract: The objective of this paper is to analyse an <i>M</i><SUP align="right"><SMALL>[<i>X</i>]</SMALL></SUP>/<i>G</i>(<i>a</i>, <i>b</i>)/1 queueing model with second optional service, multiple vacation, state dependent arrival and optional re-service. After completing the first essential service, a batch of customers either requests for re-service or leaves the system without re-service. After the completion of first essential service (with or without re-service), the batch of customers either requests for second optional service or leaves the system. At the completion moment of second optional service, the batch of customers either requests for re-service or leaves the system after the second service. Whenever the queue size is less than '<i>a</i>', the server commences vacation. At the instant of vacation completion, if at least '<i>a</i>' customers wait for service, the server starts a busy period. Otherwise, the server resumes another vacation. Using supplementary variable technique, the steady-state probability generating function (PGF) of the queue size is obtained. Journal: Int. J. of Operational Research Pages: 254-278 Issue: 2 Volume: 44 Year: 2022 Keywords: bulk queue; second optional service; multiple vacation; optional re-service; state dependent arrival. File-URL: http://www.inderscience.com/link.php?id=123393 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:2:p:254-278 Template-Type: ReDIF-Article 1.0 Author-Name: Sejal Satish Dhage Author-X-Name-First: Sejal Satish Author-X-Name-Last: Dhage Author-Name: Vaibhav S. Narwane Author-X-Name-First: Vaibhav S. Author-X-Name-Last: Narwane Author-Name: Rakesh D. Raut Author-X-Name-First: Rakesh D. Author-X-Name-Last: Raut Author-Name: Niraj Kishore Dere Author-X-Name-First: Niraj Kishore Author-X-Name-Last: Dere Author-Name: Bhaskar B. Gardas Author-X-Name-First: Bhaskar B. Author-X-Name-Last: Gardas Author-Name: Balkrishna E. Narkhede Author-X-Name-First: Balkrishna E. Author-X-Name-Last: Narkhede Title: Mathematical programming model to optimise an environmentally constructed supply chain: a genetic algorithm approach Abstract: The purpose of the study is to develop a network model for effective decision making from the sustainability aspect. The study proposes a mathematical programming model to optimise an environmentally constructed supply chain. The effect on the environment by components such as carbon monoxide, nitrogen dioxide and volatile organic particles formed during transportation in the supply chain has been considered. The multi-objective genetic algorithm optimises total cost and total environmental impact, which were subjected to constraints of demand, return, flow balance and capacity. The total cost consists of purchase cost, fixed cost, transportation cost, manufacturing cost, processing cost and inventory cost. Environmental impact of production, transportation, handling, lead reclamation, and plastic recycling process was considered. The model also uses life cycle assessment-based method for quantification of environmental impact and establishes Pareto optimal solutions for minimisation of economic as well as environmental impact. Results show a considerable reduction in closed-loop supply chain cost. Journal: Int. J. of Operational Research Pages: 226-253 Issue: 2 Volume: 44 Year: 2022 Keywords: reverse logistics; RL; closed-loop supply chain; CLSC; life cycle assessment; LCA; battery recycling; SLI batteries; environmental supply chain impact; multi-objective optimisation; genetic algorithm; artificial intelligence. File-URL: http://www.inderscience.com/link.php?id=123395 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:2:p:226-253 Template-Type: ReDIF-Article 1.0 Author-Name: Mohammad Reza Marjani Author-X-Name-First: Mohammad Reza Author-X-Name-Last: Marjani Author-Name: Mohammad Habibi Author-X-Name-First: Mohammad Author-X-Name-Last: Habibi Author-Name: Arash Pazhouhandeh Author-X-Name-First: Arash Author-X-Name-Last: Pazhouhandeh Title: An innovative hybrid fuzzy TOPSIS based on design of experiments for multi-criteria supplier evaluation and selection Abstract: In this article, nine essential criteria are considered to select the best supplier in supply chain risk management. For this purpose, to address the unspecified criteria and the analysis of the results, a mixed approach of fuzzy TOPSIS and design of experiments (DOE) were presented, and a 2<SUP align="right"><SMALL>k</SMALL></SUP> factorial design was used for factor analysis at both low and high levels. Combining the fuzzy TOPSIS and DOE gives the decision-makers more freedom to select because it can analyse the effects of different factors on the response variable by sensitivity analysis and according to different weights. The results of the analysis of variance (ANOVA) were calculated for each response variable. The obtained R<SUP align="right"><SMALL>2</SMALL></SUP> value shows that the model works well with the elimination of effects. A comparison was made to evaluate the effectiveness of the proposed method. Besides, to rank the factors based on each response variable, the Pareto chart was used that was very impressive, and the ineffective factors were eliminated. Finally, the ranking results for each decision-maker were compared with Shannon entropy weight modification method and decision-makers. Journal: Int. J. of Operational Research Pages: 171-209 Issue: 2 Volume: 44 Year: 2022 Keywords: supplier selection; supply chain risk management; fuzzy TOPSIS; design of experiments; DOE; 2k factorial design; Pareto chart; Shannon entropy; analysis of variance; ANOVA; Grey TOPSIS. File-URL: http://www.inderscience.com/link.php?id=123397 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:2:p:171-209 Template-Type: ReDIF-Article 1.0 Author-Name: Adel E. Alshayji Author-X-Name-First: Adel E. Author-X-Name-Last: Alshayji Author-Name: Mohammad A. Darwish Author-X-Name-First: Mohammad A. Author-X-Name-Last: Darwish Author-Name: Khaled A. Alali Author-X-Name-First: Khaled A. Author-X-Name-Last: Alali Title: Optimal production output and frequency of rest-breaks for a worker subjected to fatigue Abstract: It is reported by many researchers that the quality of a produced item deteriorates and work-related incidents increase when a worker is subjected to high level of fatigue at the workplace. As a direct consequence, the daily production yield of the worker decreases which, in turn, reduces the profit of a company. Some authors suggested short breaks at workplace so that the worker overcomes fatigue and improve productivity. In this paper, we mathematically study this relationship and develop a model that finds the optimal work-rest schedule such that the daily production output of a worker is maximised. We assume that the worker fatigue increases exponentially during work. Unlike the models used in the literature, the reduction in the worker fatigue level is modelled by utilising concepts in the preventive maintenance engineering literature. This simplifies the solution method significantly. Journal: Int. J. of Operational Research Pages: 125-139 Issue: 1 Volume: 45 Year: 2022 Keywords: operations research; production; worker safety; rest-breaks; worker fatigue; exponential fatigue function. File-URL: http://www.inderscience.com/link.php?id=125717 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:45:y:2022:i:1:p:125-139 Template-Type: ReDIF-Article 1.0 Author-Name: Azahar Alam Author-X-Name-First: Azahar Author-X-Name-Last: Alam Author-Name: Chandan Bhar Author-X-Name-First: Chandan Author-X-Name-Last: Bhar Title: Development of a heuristic for no-wait flowshop production process to minimise makespan Abstract: The no-wait flow shop production system has much practical application in manufacturing industries. Minimisation total completion time of the no-wait flow shop comes under the NP-complete category. Consequently, getting effective and efficient quality of results for such types of problems is a challenge where effective means optimal or near-optimal solutions and efficient means, which took less computational time. A heuristic is proposed for the problem under consideration, in which the problem is getting solved in two phases. The first phase is the initial sequence generation, and the second one is the improvement phase. Proposed heuristic is compared with four best-known heuristics, and for the smaller size problem, an exhaustive enumeration is used to check the performance of the heuristic. Moreover, heuristic performance is tested over 600 randomly generated problems of different sizes. For large size problems, it showed that the proposed heuristic is very much efficient. Journal: Int. J. of Operational Research Pages: 68-85 Issue: 1 Volume: 45 Year: 2022 Keywords: heuristic; no-wait flowshop problem; makespan minimisation; computational complexity. File-URL: http://www.inderscience.com/link.php?id=125719 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:45:y:2022:i:1:p:68-85 Template-Type: ReDIF-Article 1.0 Author-Name: Domingo Pavolo Author-X-Name-First: Domingo Author-X-Name-Last: Pavolo Author-Name: Delson Chikobvu Author-X-Name-First: Delson Author-X-Name-Last: Chikobvu Title: Jackknifing then model averaging: investigating the improvements to fitness to data and prediction accuracy of two-input under-fitted and just-fitted response models Abstract: The possibility of improving the fitness to data and prediction accuracy of models in a multi-response surface methodology environment of under and just-fitted ordinary least squares response models by jackknifing then combining the resultant partial estimates and the pseudo-values using arithmetic averaging or criterion-based frequentist model averaging was investigated. Jackknifing is known to reduce parametric and model bias. Model averaging is known to reduce model bidirectional bias and variance. A typical multi-response surface methodology dataset and resultant validation dataset were used as example. Results suggest that it is possible to obtain better fitness to data and prediction accuracy by jackknifing a just-fitted response model of interest and combining the resultant partial estimates using arithmetic averaging. The combining of pseudo-values using arithmetic averaging or criterion-based frequentist model averaging gave mixed results. The actual jackknife model estimators gave good performance with under-fitted models. Journal: Int. J. of Operational Research Pages: 86-106 Issue: 1 Volume: 45 Year: 2022 Keywords: multiresponse surface methodology; jackknifing; partial estimates; pseudo-values; arithmetic model averaging; criterion-based frequentist model averaging; CBFMA; prediction accuracy. File-URL: http://www.inderscience.com/link.php?id=125720 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:45:y:2022:i:1:p:86-106 Template-Type: ReDIF-Article 1.0 Author-Name: Fabio Gobbi Author-X-Name-First: Fabio Author-X-Name-Last: Gobbi Title: The problem of detecting nonlinearity in time series generated by a state-dependent autoregressive model: a simulation study Abstract: The aim of the paper is to try to measure, through a Monte Carlo experiment, nonlinearity in time series generated by a strictly stationary and uniformly ergodic state-dependent autoregressive process. The model under study is intrinsically nonlinear but the choice of parameters strongly impacts on the type of serial dependence making its identification complicated. For this reason, the paper exploits two statistical tests of independence and linearity in order to select the parameter values which ensure the joint rejection of both hypothesis. After that, our study uses two measures of nonlinear dependence in time series recently introduced in the literature, the auto-distance correlation function and the autodependence function, in order to identify nonlinearity induced by the proposed model. Journal: Int. J. of Operational Research Pages: 107-124 Issue: 1 Volume: 45 Year: 2022 Keywords: nonlinear time series; independence test; linearity test; auto-distance correlation; autodependogram. File-URL: http://www.inderscience.com/link.php?id=125721 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:45:y:2022:i:1:p:107-124 Template-Type: ReDIF-Article 1.0 Author-Name: K. Senbagam Author-X-Name-First: K. Author-X-Name-Last: Senbagam Author-Name: M. Kokilamani Author-X-Name-First: M. Author-X-Name-Last: Kokilamani Title: A partial back-ordering inventory model for log-gamma deteriorating items with quadratic demand and shortages Abstract: In this paper, an inventory model for deteriorating items with quadratic demand is developed. Deterioration is permitted on inventory that follows two-parameter of log-gamma distributions. In the inventory system, shortages are taken as fully and partially back-ordered. The goal is to find the optimal cycle times in order to minimise total costs. First, the problem formulation and procedure are explained to find the most favourable solution. The solution process is also found to minimise the overall cost. Various principles are used to calculate the total costs. A solution process is developed and numerical examples are presented to demonstrate the outcome of the proposed model in order to find the optimal solution. Sensitivity analysis of the most favourable solution with respect to system parameters is performed and recommendations are provided for further research. Journal: Int. J. of Operational Research Pages: 210-225 Issue: 2 Volume: 44 Year: 2022 Keywords: inventory system; deteriorating items; log-gamma deterioration; quadratic function; partial back-ordering; shortages. File-URL: http://www.inderscience.com/link.php?id=123418 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:2:p:210-225 Template-Type: ReDIF-Article 1.0 Author-Name: Avijit Duary Author-X-Name-First: Avijit Author-X-Name-Last: Duary Author-Name: Nirmal Kumar Author-X-Name-First: Nirmal Author-X-Name-Last: Kumar Author-Name: Md. Akhtar Author-X-Name-First: Md. Author-X-Name-Last: Akhtar Author-Name: Ali Akbar Shaikh Author-X-Name-First: Ali Akbar Author-X-Name-Last: Shaikh Author-Name: Asoke Kumar Bhunia Author-X-Name-First: Asoke Kumar Author-X-Name-Last: Bhunia Title: Real coded self-organising migrating genetic algorithm for nonlinear constrained optimisation problems Abstract: The objective of this article is to propose a new hybrid algorithm named as real coded self-organising migrating genetic algorithm (C-RCSOMGA) by combining real coded genetic algorithm (RCGA) and modified self-organising migrating algorithm (SOMA) for solving the nonlinear constrained optimisation problems. In RCGA, a modified mutation operator called as double mutation operator has been introduced combining two different existing mutation operators, whereas in SOMA, a modified strategy has been proposed. To test the performance of the proposed algorithm, a set of test problems taken from the existing literature has been solved and the simulated results have been compared numerically as well as graphically with the existing algorithms. In the graphical comparison, a modification of performance index (PI) has been made. Finally, with the help of modified performance index (MPI), it has been shown that the proposed hybrid algorithm has performed much better than the existing algorithms. Journal: Int. J. of Operational Research Pages: 29-67 Issue: 1 Volume: 45 Year: 2022 Keywords: genetic algorithm; self-organising migrating algorithm; SOMA; performance index; nonlinear constrained optimisation; global optimisation. File-URL: http://www.inderscience.com/link.php?id=125722 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:45:y:2022:i:1:p:29-67 Template-Type: ReDIF-Article 1.0 Author-Name: Chaimongkol Limpianchob Author-X-Name-First: Chaimongkol Author-X-Name-Last: Limpianchob Author-Name: Masahiro Sasabe Author-X-Name-First: Masahiro Author-X-Name-Last: Sasabe Author-Name: Shoji Kasahara Author-X-Name-First: Shoji Author-X-Name-Last: Kasahara Title: A multi-objective optimisation model for production and transportation planning in a marine shrimp farming supply chain network Abstract: The traditional operation of marine shrimp farming is widely practiced in Southeast Asia. Giant freshwater prawn farming is one of the main types of marine shrimp farming that also still operates traditionally. Many of these farms operate without advanced techniques for production planning, inventory control, and transportation strategic decisions throughout the supply chain network which are among the most important managerial activities in commercial farming. Maintaining product freshness is of vital importance for aquaculture product. Therefore, this paper develops a multi-objective mixed-integer linear programming model for a marine shrimp farming supply chain network design problem. The problem is to plan production and control inventory according to constraints while maximise total profit surplus and minimise shortest route. A multi-echelon, multi-facility, and multi-period mathematical model is proposed such that real conditions are considered. In the end, some numerical illustrations are provided to show the proper Pareto solutions considering all of the objectives for the decision maker. Journal: Int. J. of Operational Research Pages: 1-28 Issue: 1 Volume: 45 Year: 2022 Keywords: supply chain network; SCN; mixed-integer linear programming; marine shrimp farming; giant freshwater prawns; multi-objective optimisation. File-URL: http://www.inderscience.com/link.php?id=125727 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:45:y:2022:i:1:p:1-28 Template-Type: ReDIF-Article 1.0 Author-Name: Peter E. Ezimadu Author-X-Name-First: Peter E. Author-X-Name-Last: Ezimadu Title: A mathematical model of cooperative advertising support to the followers in a manufacturer-distributor-retailer supply chain Abstract: This work considers cooperative advertising in a decentralised channel which involves a manufacturer, a distributor and a retailer. It uses Stackelberg differential game theory to model the direct involvement of both the distributor and the retailer in advertising, with the manufacturer supporting their advertising efforts through subsidy. The work uses Sethi's sales-advertising dynamics to model the market awareness share dynamics. It considers four channel structures and obtains the players' strategies and payoffs for all-four channel structures, and observes that the manufacturer should not totally subsidise any of the players' advertising effort. It further shows the manufacturer should not simultaneously support both players advertising efforts. However, if he must support both players, then a profit-sharing agreement on the channel payoffs must to be reached by the members of the supply chain to ensure that the manufacturer is not short-changed. Journal: Int. J. of Operational Research Pages: 141-170 Issue: 2 Volume: 44 Year: 2022 Keywords: cooperative advertising; decentralised channel; Stackelberg game; differential game; subsidy; Sethi's sales-advertising dynamics; advertising support; supply chain. File-URL: http://www.inderscience.com/link.php?id=123450 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:2:p:141-170 Template-Type: ReDIF-Article 1.0 Author-Name: Mohyiddine Soltani Author-X-Name-First: Mohyiddine Author-X-Name-Last: Soltani Author-Name: Hichem Aouag Author-X-Name-First: Hichem Author-X-Name-Last: Aouag Author-Name: Mohammed Djamel Mouss Author-X-Name-First: Mohammed Djamel Author-X-Name-Last: Mouss Title: A multiple criteria decision-making improvement strategy in complex manufacturing processes Abstract: The purpose of this paper is to propose an improvement strategy based on multi-criteria decision making approaches, including fuzzy analytic hierarchy process (AHP), preference ranking organisation method for enrichment evaluation II (PROMETHEE) and višekriterijumsko kompromisno rangiranje (VIKOR) for the objective of simplifying and organising the improvement process in complex manufacturing processes. Firstly, the proposed strategy started with the selection of decision makers', such as company leaders, to determine performance indicators. Then fuzzy AHP is used to quantify the weight of each defined indicators. Finally, the weights carried out from fuzzy AHP approach are used as input in VIKOR and PROMETHE II to rank the operations according to their improvement priority. The results obtained from each outranking method are compared and the best method is determined. Journal: Int. J. of Operational Research Pages: 194-218 Issue: 2 Volume: 45 Year: 2022 Keywords: analytic hierarchy process; AHP; višekriterijumsko kompromisno rangiranje; VIKOR; fuzzy logic; PROMETHE; improvement priority; complex manufacturing process. File-URL: http://www.inderscience.com/link.php?id=126075 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:45:y:2022:i:2:p:194-218 Template-Type: ReDIF-Article 1.0 Author-Name: P. Raghuram Author-X-Name-First: P. Author-X-Name-Last: Raghuram Author-Name: Sabiq Sulaiman Author-X-Name-First: Sabiq Author-X-Name-Last: Sulaiman Title: Agent-based modelling in capacitated lot sizing problem with sequence dependent setup time Abstract: Setups are indispensable in production, but consume substantial amount of productive time. It is vital to consider sequence dependent setup times for determining production lots to satisfy demand for diversified product types on parallel production lines. Optimisation is the most used technique to generate detailed schedules for such sequencing problems. But the feasibility of the solution is not guaranteed under uncertainty conditions. Thus, evaluating the solution configurations under uncertainty is necessary to confirm feasibility. In this paper, an agent-based, discrete event simulation technique is used to develop a flow-shop model which faces demand for multiple product varieties, and has sequence dependent setup time. The simulation aims at evaluating various results in the solution space of an optimisation model to check their feasibility under various uncertain conditions in the form of setup time, processing time, and demand. Based on the results obtained from this optimisation-simulation model, it was observed that uncertainty influences cost parameters such as overtime cost, holding cost, and lost sales. Journal: Int. J. of Operational Research Pages: 171-193 Issue: 2 Volume: 45 Year: 2022 Keywords: agent-based modelling; ABM; capacitated lot sizing model; flow-shop scheduling; optimisation; simulation. File-URL: http://www.inderscience.com/link.php?id=126076 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:45:y:2022:i:2:p:171-193 Template-Type: ReDIF-Article 1.0 Author-Name: Naciye Tuba Yılmaz Soydan Author-X-Name-First: Naciye Tuba Yılmaz Author-X-Name-Last: Soydan Author-Name: Ahmet Mete Çilingirtürk Author-X-Name-First: Ahmet Mete Author-X-Name-Last: Çilingirtürk Title: Simulation-based optimisation approach to multi-choice transportation problem Abstract: The classical transportation problem minimises the total costs of transportation of a unique product from various supply points (or warehouses) to demand points. The problem assumes that freight costs from source to destination are constant and that the supply and demand quantities are equal and strictly known, so the market for the product is well-balanced. It thus involves a special type of linear integer programming, which becomes stochastic since the constraints or parameters are random variables from a known or unknown distribution. Several studies have formulated well-known deterministic models under probabilistic restrictions. The transformed models mostly keep the confidence level at a given minimum constant or else minimise the error level. Also, there is a multi-choice stochastic transportation problem, which introduces several unit costs. In this study, we try to simulate Roy's (2014) multi-choice stochastic transport model with random supply and demand quantities from a given Weibull distribution and compare the results of distribution and total costs. As a result of the simulation, total cost value was estimated lower than the result of the problem. Journal: Int. J. of Operational Research Pages: 161-170 Issue: 2 Volume: 45 Year: 2022 Keywords: simulation-based optimisation; Weibull distribution; simulation; multi-choice transportation problem. File-URL: http://www.inderscience.com/link.php?id=126077 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:45:y:2022:i:2:p:161-170 Template-Type: ReDIF-Article 1.0 Author-Name: Hardik N. Soni Author-X-Name-First: Hardik N. Author-X-Name-Last: Soni Author-Name: Ashaba D. Chauhan Author-X-Name-First: Ashaba D. Author-X-Name-Last: Chauhan Title: Joint inventory, promotion and preservation decisions for deteriorating items with maximum lifetime and stochastic demand under two-level partial trade credit Abstract: This study models a joint inventory, promotional effort and preservation decision-making problem for deteriorating items with maximum lifetime under a two-level partial trade credit and allowable shortages. This paper considers a supplier-retailer-customer supply chain model which allows: 1) for settling the cost of purchasing, the supplier offers a partial trade credit to the retailer and at the same time retailer offers a partial trade credit to the customer; 2) the upstream(supplier-retailer) credit period increases sales of the supplier and revenue of the retailer, the downstream (retailer-customer) credit period not only lifts demand but also the opportunity cost; 3) the deteriorating product not only deteriorate continuously, but also have maximum lifetime and to reduce the deterioration rate we use the preservation technology; 4) price and promotional effort are dependent on random demand; 5) shortages are considered. The objective is to find the optimal promotional effort, preservation technology investment, length of time for the inventory level reaches zero and replenishment cycle strategies while maximising the total profit per unit time. Numerical examples are included to illustrate the algorithmic procedure and the effect of key parameters is studied to analyse the behaviour of the model. Journal: Int. J. of Operational Research Pages: 241-281 Issue: 2 Volume: 45 Year: 2022 Keywords: inventory; deterioration; preservation technology; promotion; maximum lifetime; partial trade credit; supply chain. File-URL: http://www.inderscience.com/link.php?id=126087 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:45:y:2022:i:2:p:241-281 Template-Type: ReDIF-Article 1.0 Author-Name: Farzaneh Mohammadi Author-X-Name-First: Farzaneh Author-X-Name-Last: Mohammadi Author-Name: Maryam Esmaeili Author-X-Name-First: Maryam Author-X-Name-Last: Esmaeili Author-Name: Jiang Zhang Author-X-Name-First: Jiang Author-X-Name-Last: Zhang Title: A two-level supply chain with price sensitive random demand, random yield, inspection, and rework process Abstract: This paper studies a two-level supply chain consisting of a manufacturer and a retailer. The manufacturer's production output follows a random process due to the presence of defective goods. The retailer faces random price dependent customer demand. We propose that the manufacturer can reduce the number of defective items manufactured by performing inspection and rework. We first consider both decentralised and centralised supply chain models to determine the manufacturer's production size, the retailer's order quantity, the manufacturer's rework cost, and the retailer's sales price, as well as the retailer's and the manufacturer's expected profit. We then propose a buy-back contract to coordinate the supply chain. We also use numerical examples to illustrate our findings and perform sensitivity analysis on the price elasticity of the demand and tensile coefficient of rework cost. Journal: Int. J. of Operational Research Pages: 219-240 Issue: 2 Volume: 45 Year: 2022 Keywords: random yield; random demand; rework process; pricing; supply chain; buy-back contract. File-URL: http://www.inderscience.com/link.php?id=126090 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:45:y:2022:i:2:p:219-240 Template-Type: ReDIF-Article 1.0 Author-Name: Mohsen Abdolhosseinzadeh Author-X-Name-First: Mohsen Author-X-Name-Last: Abdolhosseinzadeh Title: A lower bound competitive ratio for the online stochastic shortest path problem Abstract: In online networks, some parameters are not initially known by decision-makers, especially arc costs that are revealed over time, thereby online decisions are made without complete knowledge of the future events. Three kinds of statistical information are available in terms of online manner arrival of the last traversed nodes: the exact traversed length, the average shortest path length and the shortest path length. Three different stochastic models are considered and the related stochastic online decision criteria are obtained, such that the best competitive ratio is 2.3130. Under the assumption that the online decision-maker is informed about the intervals of the arc costs, and after some constant competitive ratios are produced, 2.3130 is determined as the best obtained lower bound competitive ratio against some previous works. Journal: Int. J. of Operational Research Pages: 119-130 Issue: 1/2 Volume: 43 Year: 2022 Keywords: online stochastic network; online decision problem; competitive analysis; online stochastic shortest path; O-SSP. File-URL: http://www.inderscience.com/link.php?id=121483 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:1/2:p:119-130 Template-Type: ReDIF-Article 1.0 Author-Name: Hassan Sarhadi Author-X-Name-First: Hassan Author-X-Name-Last: Sarhadi Author-Name: David M. Tulett Author-X-Name-First: David M. Author-X-Name-Last: Tulett Author-Name: Manish Verma Author-X-Name-First: Manish Author-X-Name-Last: Verma Title: A tri-level mixed-integer program for the optimal fortification of a rail intermodal terminal network Abstract: Rail-truck intermodal transportation plays an important role in moving freight in North America, and hence the availability and appropriate functionality of the associated infrastructure is crucial. To this end, one of the strategies to mitigate (minimise) the adverse impacts from (intentional/random) disruption prescribes fortifying a set of rail intermodal terminals so that continuity in its service is ensured. In this paper, we re-visit the tri-level model to protect a given number of rail intermodal terminals such that the effect of worst-case disruptions is minimised. We propose a tabu search metaheuristic to solve the outer level problem, and then combine it with a decomposition-based technique to solve the entire model. The proposed methodology is tested on the problem instances introduced in an earlier work, and to comment on the computational efficiency vis-à-vis the existing techniques in the literature. Journal: Int. J. of Operational Research Pages: 65-95 Issue: 1/2 Volume: 43 Year: 2022 Keywords: intermodal transportation; mixed-integer program; fortification; tabu-search metaheuristic; decomposition. File-URL: http://www.inderscience.com/link.php?id=121484 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:1/2:p:65-95 Template-Type: ReDIF-Article 1.0 Author-Name: Neelanjana Rajput Author-X-Name-First: Neelanjana Author-X-Name-Last: Rajput Author-Name: Anand Chauhan Author-X-Name-First: Anand Author-X-Name-Last: Chauhan Author-Name: R.K. Pandey Author-X-Name-First: R.K. Author-X-Name-Last: Pandey Title: Optimisation of finite economic production quantity model under cloudy normalised triangular fuzzy number Abstract: This study introduced an economic production quantity (EPQ) model with a finite production rate established for cloudy normalised triangular fuzzy number (CNTFN). In real-life situations, the goals and inventory parameters are not precise. Such type of uncertainty may be characterised by fuzzy numbers. The main objective of this research effort is to develop a mathematical model and optimise EPQ with different environment-like crisp, general fuzzy and cloudy fuzzy situations. A novel defuzzification methodology has been used for EPQ by Yager's ranking index method. Here, the constraint goal and the inventory cost parameters are assumed to be triangular-shaped fuzzy numbers with different types of left and right membership functions. The cost functions associated to these models are verified to be convex, and optimal criteria are established in all three situations. The models are numerical, graphically demonstrated and sensitivity analysis shows a decent explanation. The paper also discusses the applications and future scope of the CNTFN model in realistic situations such as when items are not easy to replenish due to some transport problem and some problems in geographically hilly regions. Journal: Int. J. of Operational Research Pages: 168-187 Issue: 1/2 Volume: 43 Year: 2022 Keywords: fuzzy optimisation; decision making; cloud fuzzy number; EPQ inventory model; finite production; extended Yager's ranking index method. File-URL: http://www.inderscience.com/link.php?id=121485 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:1/2:p:168-187 Template-Type: ReDIF-Article 1.0 Author-Name: Jyotirmoy Dalal Author-X-Name-First: Jyotirmoy Author-X-Name-Last: Dalal Title: A strategic donor-beneficiary assignment problem under supply and demand uncertainties Abstract: Considering the coexistence of food insecurity and food waste issues in the society, in collaboration with an NPO in India, we develop a novel donor-beneficiary strategic assignment model to connect the students of a set of volunteering schools with a set of habitats having under-nourished children up to 14 years of age, via a social-awareness-raising long-term endeavour. Our two-stage stochastic programming model - by addressing the demand and supply uncertainties using discrete scenarios - determines optimal strategic connections to minimise the associated connection-establishment cost as well as the expected shortage cost at the habitats due to unmet demand. We present a small but realistic test case, conduct a sensitivity analysis to illustrate the underlying trade-offs among various components, and highlight how the decision-maker can adjust system-flexibility by altering certain model parameters. Journal: Int. J. of Operational Research Pages: 188-207 Issue: 1/2 Volume: 43 Year: 2022 Keywords: food insecurity; food waste; donor; beneficiary; strategic assignment; supply and demand uncertainty; stochastic programming; scenario; mixed-integer programming model; non-profit organisation. File-URL: http://www.inderscience.com/link.php?id=121486 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:1/2:p:188-207 Template-Type: ReDIF-Article 1.0 Author-Name: S. Sivanandham Author-X-Name-First: S. Author-X-Name-Last: Sivanandham Author-Name: M.S. Gajanand Author-X-Name-First: M.S. Author-X-Name-Last: Gajanand Title: Comparison of platoon formations using departure time coordination heuristic Abstract: A platoon is a set of virtually linked vehicles that drive closely behind one another. Truck platooning is a fast-emerging application of connected and autonomous vehicles to help tackle the traditional problems of the transportation industry and to reduce cost and minimise emissions. In this study, we present a departure time coordination-based heuristic solution for platoon formations. We also use a comprehensive modal emission model to understand the influence of velocity and size of the platoon on fuel savings potential. We compare the heuristic's performance on the different variants of the platooning problem in the literature with a central platoon coordinator and analyse the impact of the type of vehicle, speed, load and size of the platoon on the fuel savings potential of platooning. Numerical analysis shows a fuel savings potential of 6.4% to 8% for a platoon size of three with heavy duty vehicles at 80 kmph for different configurations. Journal: Int. J. of Operational Research Pages: 96-118 Issue: 1/2 Volume: 43 Year: 2022 Keywords: platooning; fuel savings; routing problem; departure time; platoon formation; freight transportation. File-URL: http://www.inderscience.com/link.php?id=121487 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:1/2:p:96-118 Template-Type: ReDIF-Article 1.0 Author-Name: Ankit Chouksey Author-X-Name-First: Ankit Author-X-Name-Last: Chouksey Author-Name: Anil Kumar Agrawal Author-X-Name-First: Anil Kumar Author-X-Name-Last: Agrawal Author-Name: Ajinkya N. Tanksale Author-X-Name-First: Ajinkya N. Author-X-Name-Last: Tanksale Title: An optimisation and simulation hybrid approach for maternal healthcare facility location-allocation in the Indian context Abstract: This paper addresses availability and accessibility issues related to maternal healthcare facilities, basic healthcare to neonatal services, prevailing in India. These issues are resolved on economic considerations in establishing and running healthcare facilities and the expenses on travel incurred by mothers-to-be (MTBs) in visiting them. This problem is formulated as a mixed-integer linear programming model for determining optimal number and locations of the healthcare facilities within the specified geographical boundary. The formulation also decides optimal allocation of MTBs to these centres that are available within a reasonable distance. Service demand of MTBs was considered to be deterministic. In reality, the demand, being random, may be high to cause healthcare service quality negatively. Hybrid optimisation and simulation approach shows that the service quality deteriorates with the increase in demand variability or its mean value. This information is useful in deciding additional number of the service facilities to provide satisfactory service quality. Journal: Int. J. of Operational Research Pages: 42-64 Issue: 1/2 Volume: 43 Year: 2022 Keywords: optimisation and simulation; maternal healthcare; facility location; mixed-integer linear programming; MILP; Monte Carlo simulation. File-URL: http://www.inderscience.com/link.php?id=121489 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:1/2:p:42-64 Template-Type: ReDIF-Article 1.0 Author-Name: Mehdi Biuki Author-X-Name-First: Mehdi Author-X-Name-Last: Biuki Author-Name: Parisa Mostafazadeh Author-X-Name-First: Parisa Author-X-Name-Last: Mostafazadeh Author-Name: Shiva Zandkarimkhani Author-X-Name-First: Shiva Author-X-Name-Last: Zandkarimkhani Author-Name: Hassan Mina Author-X-Name-First: Hassan Author-X-Name-Last: Mina Title: A chance constrained closed-loop supply chain network design considering inventory-location problem Abstract: The design of reverse supply chain networks is one of the major solutions for the reduction of solid waste and use of resources for producing product to a lesser extent. The design of a reverse supply chain network leads to reduced costs in addition to reducing environmental detrimental effects. Therefore, this paper seeks to develop a mixed integer linear programming (MILP) model for designing a closed-loop supply chain network (CLSCN) under uncertainty. The study network is multi-product, multi-period and multi-echelon wherein the possibility of storage and facing shortage in the back-order type has been considered. An approach based on chance constrained is applied for controlling uncertainty. In order to investigate the efficiency of the proposed model, we implemented it in an automotive manufacturing industry in Iran where the results of model implementation through real-world data in GAMS software, as well as the results of sensitivity analysis of demand values indicate the precise function and the accuracy of the results. Journal: Int. J. of Operational Research Pages: 102-121 Issue: 1 Volume: 44 Year: 2022 Keywords: closed-loop supply chain; inventory-location problem; mathematical programming model; chance constrained theory. File-URL: http://www.inderscience.com/link.php?id=123025 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:1:p:102-121 Template-Type: ReDIF-Article 1.0 Author-Name: Venkataramanaiah Saddikuti Author-X-Name-First: Venkataramanaiah Author-X-Name-Last: Saddikuti Author-Name: Mukund Nilakantan Janardhanan Author-X-Name-First: Mukund Nilakantan Author-X-Name-Last: Janardhanan Author-Name: Vigneshwar Pesaru Author-X-Name-First: Vigneshwar Author-X-Name-Last: Pesaru Title: Hybrid multi-objective evolutionary algorithm for solving RALB-II problem Abstract: In this paper, we propose an MIP model for minimisation of cycle time and total assembly line cost simultaneously. Due to NP-hard nature of RALB (Rubinovitz and Bukchin, 1991), and to avoid local minima, a hybrid multi-objective evolutionary (H-MOE) algorithm developed based on the features of NSGA-II and simulated annealing algorithm is used to solve the RALB-II problem. Performance of the proposed algorithm is evaluated using datasets from Mukund et al. (2017b) and it was found that H-MOE algorithm outperformed the algorithm by Mukund et al. (2017b) in five out of seven cases on saving in cycle time and four out of seven in terms of total cost saving. In terms of average improvement, the proposed algorithm outperformed in terms total cost saving and underperformed in terms of time cycle compared with the performance of algorithm by Mukund et al. (2017b). Conclusions and future scope are highlighted. Journal: Int. J. of Operational Research Pages: 131-149 Issue: 1/2 Volume: 43 Year: 2022 Keywords: hybrid algorithm; multi-objective; non-dominated sorting genetic algorithm; NSGA; robotic assembly line; RAL; parameter tuning. File-URL: http://www.inderscience.com/link.php?id=121490 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:1/2:p:131-149 Template-Type: ReDIF-Article 1.0 Author-Name: Dong Dai Author-X-Name-First: Dong Author-X-Name-Last: Dai Author-Name: Arka P. Ghosh Author-X-Name-First: Arka P. Author-X-Name-Last: Ghosh Author-Name: Keguo Huang Author-X-Name-First: Keguo Author-X-Name-Last: Huang Title: Comparing time-stable performance of staffing methods using real call-centre data Abstract: A central question in capacity management for service systems is to decide the number of servers that changes over time to accommodate time-varying arrivals and maintain a prescribed service-quality level. Two common methods for this are: square-root-staffing formula (SRSF) and iterative-staffing algorithm (ISA). We examine the stability of these two methods on simulated data from a probabilistic model and on a synthetic data created by resampling actual arrival, service and abandonment times from the call-centre of an Israeli bank. We use the <i>delay probability</i> as well as other common measures for the quality of service. In the simulated case, the ISA method marginally outperforms the SRSF method in maintaining the stability around the target delay probability. But in the case of synthetic resampled data, the stability drops when the service and patience rates are large. We also give theoretical proofs for the convergence of the ISA method under appropriate conditions. Journal: Int. J. of Operational Research Pages: 1-33 Issue: 1 Volume: 44 Year: 2022 Keywords: capacity planning; staffing; call-centres; re-sampling; data analysis; queues with time varying arrivals. File-URL: http://www.inderscience.com/link.php?id=123026 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:1:p:1-33 Template-Type: ReDIF-Article 1.0 Author-Name: Berhanu Belay Author-X-Name-First: Berhanu Author-X-Name-Last: Belay Author-Name: Srikumar Acharya Author-X-Name-First: Srikumar Author-X-Name-Last: Acharya Author-Name: Rajashree Mishra Author-X-Name-First: Rajashree Author-X-Name-Last: Mishra Title: Application of multi-objective probabilistic fractional programming problem in production planning Abstract: This paper presents the application of multi-objective probabilistic fractional programming problem in production planning. The production planning model for a manufacturing company that produce multiple products with a specified period is formulated by considering some of the parameters in the right hand sides of the constraints as random variables following continuous distribution namely gamma distribution. The formulated mathematical model is a multi-objective probabilistic fractional programming problem. In the solution procedure, the deterministic equivalent of the probabilistic programming problem has not been obtained. The analytical method for multi-objective fractional programming problem has also not been applied to solve the proposed model. A stochastic simulation-based genetic algorithm is applied to solve the proposed model directly. A set of Pareto optimal solutions is obtained for the formulated production planning problem. Journal: Int. J. of Operational Research Pages: 150-167 Issue: 1/2 Volume: 43 Year: 2022 Keywords: genetic algorithm; multi-objective programming problem; probabilistic programming problem; stochastic simulation; fractional programming; production planning; gamma distribution; Pareto optimal solution. File-URL: http://www.inderscience.com/link.php?id=121491 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:1/2:p:150-167 Template-Type: ReDIF-Article 1.0 Author-Name: S.K. Yadav Author-X-Name-First: S.K. Author-X-Name-Last: Yadav Author-Name: Dinesh K. Sharma Author-X-Name-First: Dinesh K. Author-X-Name-Last: Sharma Author-Name: Kate Brown Author-X-Name-First: Kate Author-X-Name-Last: Brown Title: Estimating peppermint oil yields with auxiliary variable information Abstract: In this article, we propose an improved method for estimation of the population mean using an auxiliary variable and apply it to the peppermint oil yield for a block level in the Barabanki District of Uttar Pradesh State in India. We consider a new family of estimators for the population mean, using the area of the peppermint field as the auxiliary variable. We study the sampling properties of the proposed family, through the bias and the mean squared error (MSE) to the first order approximation. We compare the suggested estimators with competing estimators theoretically and verify the conditions under which they outperform the competing estimators with actual data collected from the Siddhaur Block of the Barabanki District. Journal: Int. J. of Operational Research Pages: 122-139 Issue: 1 Volume: 44 Year: 2022 Keywords: study variable; auxiliary variable; regression-cum-ratio estimator; bias; mean squared error; MSE; percentage relative efficiency; PRE. File-URL: http://www.inderscience.com/link.php?id=123027 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:1:p:122-139 Template-Type: ReDIF-Article 1.0 Author-Name: Justin Britt Author-X-Name-First: Justin Author-X-Name-Last: Britt Author-Name: Xiangyong Li Author-X-Name-First: Xiangyong Author-X-Name-Last: Li Author-Name: Ahmed Azab Author-X-Name-First: Ahmed Author-X-Name-Last: Azab Author-Name: Mohammed Fazle Baki Author-X-Name-First: Mohammed Fazle Author-X-Name-Last: Baki Title: Stochastic goal programming and metaheuristics for the master surgical scheduling problem Abstract: Planning and scheduling in a hospital require the consideration of several competing objectives, stakeholders, and resources. In this paper, methods for the master surgical scheduling problem (MSSP), which involves assigning surgeons to time blocks in operating rooms (ORs), are proposed. A stochastic weighted goal programming model (WGPM) with four goals and metaheuristics are used to perform elective surgery scheduling under uncertainty of both surgical durations and patient lengths of stay. In addition, discrete event simulation (DES) models and a decision support system (DSS) are developed. Computational experiments are used to evaluate the WGPM, validate the DES models, assess the relationships between the goals, and to tune and evaluate the metaheuristics. Results show that even though there are trade-offs between the goals that must be considered, it is possible to attain a high level of OR utilisation while meeting strategic targets and optimising recovery ward (RW) utilisation. Journal: Int. J. of Operational Research Pages: 5-41 Issue: 1/2 Volume: 43 Year: 2022 Keywords: operating room planning and scheduling; tactical planning; master surgical scheduling; decision support system; DSS; stochastic goal programming; discrete event simulation; DES. File-URL: http://www.inderscience.com/link.php?id=121492 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:1/2:p:5-41 Template-Type: ReDIF-Article 1.0 Author-Name: Alka Arya Author-X-Name-First: Alka Author-X-Name-Last: Arya Author-Name: Shiv Prasad Yadav Author-X-Name-First: Shiv Prasad Author-X-Name-Last: Yadav Title: Development of IFDEA models for IF input-oriented mix efficiency: case of hospitals in India Abstract: In conventional input-oriented mix efficiency (IOME), the input-output data are crisp numbers. But these data fluctuate in real world applications. Intuitionistic fuzzy set (IFS) theory can be used to solve such problem. In this paper, models are proposed to determine intuitionistic fuzzy input-oriented mix-efficiency (IFIOME) with IF input and IF output data. For determining IFIOME, intuitionistic fuzzy input-oriented CCR (IFIOCCR) model and intuitionistic fuzzy input-oriented slack-based measure (IFIOSBM) model are proposed with IF input-output data. These models are solved by using expected values of intuitionistic fuzzy numbers (IFNs). Based on IFIOME, a ranking method is developed to rank the DMUs. Also, the intuitionistic fuzzy correlation coefficient (IFCC) between IF variables is proposed to validate the proposed models. To validate the proposed models, an illustrative example and a health sector application are presented. Journal: Int. J. of Operational Research Pages: 34-57 Issue: 1 Volume: 44 Year: 2022 Keywords: intuitionistic fuzzy input-oriented CCR model; intuitionistic fuzzy input-oriented SBM model; intuitionistic fuzzy input-oriented mix-efficiency; IFIOME; hospital efficiency. File-URL: http://www.inderscience.com/link.php?id=123028 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:1:p:34-57 Template-Type: ReDIF-Article 1.0 Author-Name: Shivshanker Singh Patel Author-X-Name-First: Shivshanker Singh Author-X-Name-Last: Patel Title: Informal-contract farming in an agriculture supply chain: a game-theoretic analysis Abstract: A contract in an agriculture supply-chain under market uncertainty leads to renege. Specially, when the contract enforcement cost is not very high it is prone to collapse. In this paper, a set of game theoretical models have been employed to analyse renege of the contract farming (informal-contract). To start with a normal form game-theoretic model with pure strategies has been utilised to model the price risk of the market and determine the outcomes for the players (firm and farmer); subsequently, a mixed strategy model has been studied. Owing to incomplete information under the informal-contract, a Bayesian Nash equilibrium and mixed strategy Bayesian Nash equilibrium have also been analysed. The results have been explained with an example of tomato contract farming of Southern India. From the business standpoint the results and renegotiation framework presented in this paper can be utilised to avoid a renege and dispute in the contract farming. Journal: Int. J. of Operational Research Pages: 208-225 Issue: 1/2 Volume: 43 Year: 2022 Keywords: contract farming; informal contract; supply chain; Bayesian Nash equilibrium; re-negotiation. File-URL: http://www.inderscience.com/link.php?id=121493 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:1/2:p:208-225 Template-Type: ReDIF-Article 1.0 Author-Name: Noureddine Jilani Ben Naouara Author-X-Name-First: Noureddine Jilani Ben Author-X-Name-Last: Naouara Author-Name: Faouzi Trabelsi Author-X-Name-First: Faouzi Author-X-Name-Last: Trabelsi Title: New class of optimal multiple stopping times problems Abstract: This paper is devoted to study a new discounted nonlinear optimal multiple stopping times problem with discounted factor <i>β</i> > 0 and infinite horizon. Under the right continuity of the underlying process, we show that the problem can be reduced to a sequence of ordinary optimal stopping problems. Next in the Markovian case, we characterise the value function of the problem in terms of <i>β</i>-excessive functions. Finally, in the special case of a diffusion process, we give explicit expressions for the value function of the problem as well as the optimal stopping strategy. As an explicit example in finance, we apply our theoretical results to manage a new generalised swing contract which gives its holder <i>n</i> rights to mark the price <i>X</i> of a stock, where the payment is only allowed at the final exercise time rather than at each exercise time as in the classical swing contact. Journal: Int. J. of Operational Research Pages: 226-253 Issue: 1/2 Volume: 43 Year: 2022 Keywords: optimal multiple stopping; discounted factor; Markov process; diffusion process; Snell envelope; dynamic programming; β-excessive functions; swing option; COVID-19 pandemic. File-URL: http://www.inderscience.com/link.php?id=121494 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:1/2:p:226-253 Template-Type: ReDIF-Article 1.0 Author-Name: Domingo Pavolo Author-X-Name-First: Domingo Author-X-Name-Last: Pavolo Author-Name: Delson Chikobvu Author-X-Name-First: Delson Author-X-Name-Last: Chikobvu Title: Using criterion-based model averaging in two-input multiple response surface methodology problems Abstract: Experimental designs in multiple response surface methodology (MRSM) often result in small sample size datasets with associated modelling and model selection problems. Classical model selection criteria are inefficient when using small sample size datasets while the model selection process has inherent uncertainties. Modelling of small sample size datasets below (10 + <i>k</i>), where <i>k</i> is the maximum number of regressors inclusive of the intercept, suffers from credibility problems. In this empirical paper, criterion-based frequentist model-averaging (CBFMA) is proposed as a solution to the small sample size problems of modelling MRSM datasets. We also compare the goodness of fit and prediction accuracy of using CBFMA models versus ordinary least squares (OLS) candidate models. Findings suggest that CBFMA models have good fitness to data and predictive accuracy. Also, the small sample size model selection criteria bias problem is improved on. However, in the MRSM context, CBFMA does not directly solve both criterion and response surface uncertainties, and averaged model estimators have mean squared errors that are greater than the best OLS candidate models. Journal: Int. J. of Operational Research Pages: 80-101 Issue: 1 Volume: 44 Year: 2022 Keywords: multiple response surface methodology; MRSM; experimental design; all possible regression models; frequentist criterion-based model averaging; small sample size datasets; process optimisation. File-URL: http://www.inderscience.com/link.php?id=123030 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:1:p:80-101 Template-Type: ReDIF-Article 1.0 Author-Name: Armindo Soares Author-X-Name-First: Armindo Author-X-Name-Last: Soares Author-Name: Carina Pimentel Author-X-Name-First: Carina Author-X-Name-Last: Pimentel Author-Name: Ana Moura Author-X-Name-First: Ana Author-X-Name-Last: Moura Title: Production planning and scheduling with applications in the tile industry Abstract: In this paper we consider the medium to short-term production planning and scheduling (PPS) process of a ceramic tile industry. The PPS process encompasses three problems: 1) the development of a master production plan that determines the medium-term production needs; 2) the development of a biweekly production scheduling plan that minimises the production time required to complete the set of products, so as to meet customer orders within agreed due dates and ensure the filling of connected firing kilns; 3) the available-to-promise problem (or jobs order acceptance/order selection, and delivery date establishment problem). The production scheduling problem (PSP) was addressed as an identical parallel machine problem, with machine eligibility constraints, family and subfamily setups and minimum production lot sizes. A specific heuristic and a mixed integer programming model are proposed to solve the PSP. A model-driven decision support system, applying and manipulating quantitative models, that improves the quality and time expenditure of the PPS process, is also presented. Journal: Int. J. of Operational Research Pages: 58-79 Issue: 1 Volume: 44 Year: 2022 Keywords: production planning and scheduling; PPS; constructive heuristic; decision support system; DSS; mixed integer programming; MIP; tile industry; master production schedule; MPS; available to promise; ATP; production scheduling. File-URL: http://www.inderscience.com/link.php?id=123035 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:1:p:58-79 Template-Type: ReDIF-Article 1.0 Author-Name: Abdelrahman AbuSerriya Author-X-Name-First: Abdelrahman Author-X-Name-Last: AbuSerriya Author-Name: Sadiq AbdElall Author-X-Name-First: Sadiq Author-X-Name-Last: AbdElall Author-Name: Salah R. Agha Author-X-Name-First: Salah R. Author-X-Name-Last: Agha Title: Multi-criteria optimisation of fire station location in Gaza Strip Abstract: This paper evaluated fire stations' locations and suggested how to improve their performance in Gaza Strip. The paper used two models, and these models include set covering and goal programing models. The models were solved in two stages: the first stage used the set covering model to determine the number of fire stations needed for given time limits. The output of the first model was inputted into second model to determine the fire stations' locations. Further, several scenarios were proposed for sensitivity analysis. The first scenario freely identified each fire station locations, while the second scenario was forced the model to select/unselect given locations based on decision-maker preferences. Based on the fire department's request which called for expansion through identifying the optimum number and locations of new fire stations needed at six and eight minutes, it was found that they need to build 12 new stations and six new stations, respectively. Journal: Int. J. of Operational Research Pages: 397-417 Issue: 3 Volume: 45 Year: 2022 Keywords: fire stations location; goal programming; Gaza Strip; multi-criteria; set covering. File-URL: http://www.inderscience.com/link.php?id=127134 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:45:y:2022:i:3:p:397-417 Template-Type: ReDIF-Article 1.0 Author-Name: Yanni Ping Author-X-Name-First: Yanni Author-X-Name-Last: Ping Author-Name: Seung-Lae Kim Author-X-Name-First: Seung-Lae Author-X-Name-Last: Kim Title: In-house production vs. outsourcing: the effect of volume-based learning on quality competition Abstract: This paper considers an original equipment manufacturer (OEM) who outsources finished products to a contract manufacturer (CM), who adopts the OEM's existing technology and achieves quality improvement through learning-by-doing. Besides the role of upstream partner, the CM also becomes a downstream competitor. We examine learning-by-doing and quality dynamically under a two-period model - both for cases when quality competition exists and does not exist. We identify the conditions under which pure outsourcing, partial outsourcing, or non-outsourcing is most advantageous. When there is no quality competition and when the CM's quality improvement does not hurt the OEM's future demand, we find that it would still be beneficial for the OEM to apply a partial outsourcing strategy. When quality competition exists, the OEM's decision in the second period follows the same pattern as the non-competition case, while the CM's wholesale price depends on the tradeoff between selling through the OEM and selling independently. Journal: Int. J. of Operational Research Pages: 344-361 Issue: 3 Volume: 45 Year: 2022 Keywords: learning-by-doing; outsourcing; quality-improvement; competitive CM. File-URL: http://www.inderscience.com/link.php?id=127135 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:45:y:2022:i:3:p:344-361 Template-Type: ReDIF-Article 1.0 Author-Name: Sina Glaeser Author-X-Name-First: Sina Author-X-Name-Last: Glaeser Author-Name: Christian Ullrich Author-X-Name-First: Christian Author-X-Name-Last: Ullrich Title: Multi-resource balancing: a case of a German kitchen manufacturer Abstract: We address a multi-resource balancing-problem at Nobilia-Werke J. Stickling GmbH % Co. KG, the leading kitchen manufacturer in Germany. Nobilia maintains no warehouse for finished goods. The customised cabinets of a kitchen are manufactured in parallel and aggregated just in sequence for loading into trucks immediately after production. A smooth flow of materials is essential to ensure on-time completion and loading of a whole kitchen. Nowadays, changing customer tastes drive customisation. Due to an increasing number of capacity restrictions in its production processes, Nobilia's current planning method has reached its limits. We present an integer programming model to address a variant of the multi-resource generalised assignment problem (MRGAP). We consider a multi-criteria objective function and a set of constraints reflecting Nobilia's requirements. We propose a software-based solution approach for Nobilia's instance sizes. The results of our computational experiments on real-world data demonstrate that our approach provides significant benefits. Journal: Int. J. of Operational Research Pages: 283-299 Issue: 3 Volume: 45 Year: 2022 Keywords: resource balancing; integer programming; multi-criteria; production planning; machine assignment; mass customisation; generalised assignment problem; GAP; real-world application; LINGO. File-URL: http://www.inderscience.com/link.php?id=127136 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:45:y:2022:i:3:p:283-299 Template-Type: ReDIF-Article 1.0 Author-Name: Md Akhtar Author-X-Name-First: Md Author-X-Name-Last: Akhtar Author-Name: Amalesh Kumar Manna Author-X-Name-First: Amalesh Kumar Author-X-Name-Last: Manna Author-Name: Avijit Duary Author-X-Name-First: Avijit Author-X-Name-Last: Duary Author-Name: Asoke Kumar Bhunia Author-X-Name-First: Asoke Kumar Author-X-Name-Last: Bhunia Title: A hybrid tournament differential evolution algorithm for solving optimisation problems and applications Abstract: The goal of this work is to propose a hybrid algorithm, combining differential evolution algorithm and tournamenting process, for solving constrained and bound-constrained optimisation problems. Considering different options of binary tournamenting, six variants of the proposed hybrid algorithm are developed. To test the efficiency and performance of the proposed hybrid algorithm, 12 benchmark optimisation problems are considered and solved. From the obtained results of these benchmark problems, the proposed six different variants of the hybrid algorithm are compared numerically as well as graphically. From these comparisons, the best variants of the hybrid algorithm for solving constrained and bound-constrained optimisation problems are identified separately. Then by using these best variants, three well known engineering design problems are considered and solved. The computational results are compared with the results of some of the existing algorithms available in the literature. In each case, it is observed that the proposed algorithm performs well. Journal: Int. J. of Operational Research Pages: 300-343 Issue: 3 Volume: 45 Year: 2022 Keywords: global optimisation; constrained optimisation; bound-constrained optimisation; differential evolution; tournamenting. File-URL: http://www.inderscience.com/link.php?id=127140 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:45:y:2022:i:3:p:300-343 Template-Type: ReDIF-Article 1.0 Author-Name: Hui-Ling Yang Author-X-Name-First: Hui-Ling Author-X-Name-Last: Yang Title: A note on 'a two-warehouse partial backlogging inventory model for deteriorating items with permissible delay in payment under inflation' Abstract: In order to satisfy the customer demand, we know that many fashionable product or high-tech electronic items do not allow shortages at the beginning of the replenishment cycle. Furthermore, if the retailer wants to satisfy the demand and increase sales, they do not hope for shortages to occur at the beginning of the replenishment cycle. Thus, an alternative inventory model which is different from Yang and Chang (2013) is proposed. The inventory model assumes that shortages are allowed at the end of the replenishment cycle, not at the beginning. The aim is also to derive the retailer's optimal replenishment policy that maximises the net present value of the profit per unit time. The presented theoretical results ensure that the optimal solution exists uniquely. Numerical examples and some comprehensive sensitivity analysis are performed. From the numerical results, if the permissible delay period is not long, the proposed model is still practical. Journal: Int. J. of Operational Research Pages: 141-160 Issue: 2 Volume: 45 Year: 2022 Keywords: two-warehouse; partial-backlogging; permissible delay in payment; inflation. File-URL: http://www.inderscience.com/link.php?id=126119 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:45:y:2022:i:2:p:141-160 Template-Type: ReDIF-Article 1.0 Author-Name: Jakob Kotas Author-X-Name-First: Jakob Author-X-Name-Last: Kotas Title: A linear programming model for airline schedule recovery after disruption Abstract: We present a decision support framework for optimal flight rescheduling on an airline's day of operations under unanticipated system disruption. We consider disruptions which add an unforeseen need to extend each aircraft's turnaround time on the ground, not necessarily uniformly across all flights or airports in the system. Our model optimally reschedules remaining flights of the day to minimise system delays and cancellations. The model is formulated as a mixed integer linear program. We prove that structural properties of the model allow it to be decomposed into a finite set of linear programs, and a computationally tractable algorithm for its solution is described. The model is solvable exactly and quickly, even for large airlines. Numerical simulations are presented for a case study of a winter weather event impacting Horizon Air, a regional airline based in the Pacific Northwest of the USA. Journal: Int. J. of Operational Research Pages: 378-396 Issue: 3 Volume: 45 Year: 2022 Keywords: decision support framework; disruption management; scheduling; airline scheduling; airline operations; linear programming; mixed integer linear programming; winter weather; snow; de-icing. File-URL: http://www.inderscience.com/link.php?id=127146 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:45:y:2022:i:3:p:378-396 Template-Type: ReDIF-Article 1.0 Author-Name: François Dubeau Author-X-Name-First: François Author-X-Name-Last: Dubeau Author-Name: Marie Emmanuel Ntigura Habingabwa Author-X-Name-First: Marie Emmanuel Ntigura Author-X-Name-Last: Habingabwa Title: Computing Pareto set in the criterion space for bicriteria linear programs using a single criterion software Abstract: In case of a mathematical programming problem with conflicting criteria, the Pareto set is a useful tool for a decision-maker. Based on the geometric properties of the Pareto set for a bicriteria linear program, we present a simple method to compute this set in the criterion space. We describe completely the algorithm and analyse its complexity. We illustrate the method by solving in details two simple examples. It is important to observe that the method requires only a basic linear program solver. Journal: Int. J. of Operational Research Pages: 437-450 Issue: 4 Volume: 43 Year: 2022 Keywords: bicriteria linear program; efficient set; Pareto set; criterion space; weighted-sum. File-URL: http://www.inderscience.com/link.php?id=122810 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:4:p:437-450 Template-Type: ReDIF-Article 1.0 Author-Name: Heibatolah Sadeghi Author-X-Name-First: Heibatolah Author-X-Name-Last: Sadeghi Author-Name: Anwar Mahmoodi Author-X-Name-First: Anwar Author-X-Name-Last: Mahmoodi Title: Multi-objective inventory model for material requirements planning with uncertain lead-time Abstract: Material requirements planning (MRP), in its original form, utilises deterministic lead-time. However, the lead-time uncertainty is a fact of life in most of production systems. Therefore, developing MRP to deal with lead-time uncertainty is of great importance to academics and practitioners. In this paper, the problem of supply planning is considered in a multi-period, multi-level assembly system in which each sub-level has several components whose lead-times are uncertain. A two-objective mathematical model is presented not only to provide the appropriate number of periods in POQ policy, but also to determine the planning lead-time of each sub-level component. The aim of the model is to minimise the expected total cost, and to maximise the customer service level. Furthermore, two metaheuristic algorithms, namely non-dominated sorting genetic algorithm-II (NSGA-II), and multi-objective particle swarm optimisation (MOPSO) are proposed to solve the model. Finally, numerical experiments are carried out to compare the effectiveness of the procedures. Journal: Int. J. of Operational Research Pages: 391-415 Issue: 4 Volume: 43 Year: 2022 Keywords: supply planning; random lead-time; customer service level; periodic order quantity; POQ; multi-objective genetic algorithm. File-URL: http://www.inderscience.com/link.php?id=122811 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:4:p:391-415 Template-Type: ReDIF-Article 1.0 Author-Name: Ritu Chandna Author-X-Name-First: Ritu Author-X-Name-Last: Chandna Title: Selecting the most agile manufacturing system with respect to agile attribute-technology-fuzzy AHP approach Abstract: Agility of manufacturing systems is defined as the manufacturing system's competence to exists and prosper in surroundings which are affected by continuous and unpredictable changes and competition. Technology plays a vital role in helping managers to achieve agile manufacturing. Technology is changing rapidly and the manufacturing systems have to change their processes, structures, products and services accordingly to survive profitability in the market. Technology has four qualities which are knowledge about technology, finding direction in modern technology, ability in making technology more effective and a resilient method of bringing out technology. This paper uses fuzzy AHP approach to select the most agile manufacturing system with respect to these technological dimensions. This research will help decision-makers to initiate technological innovations in manufacturing processes, to improve and help to control and evaluate the quality of emerging technologies and expand them for adoption. The analysis shows that flexible production technology parameter is more important as compared to other parameters. Journal: Int. J. of Operational Research Pages: 512-532 Issue: 4 Volume: 43 Year: 2022 Keywords: agility; manufacturing system; technology; competition; fuzzy AHP. File-URL: http://www.inderscience.com/link.php?id=122812 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:4:p:512-532 Template-Type: ReDIF-Article 1.0 Author-Name: Md. Amirul Islam Author-X-Name-First: Md. Amirul Author-X-Name-Last: Islam Author-Name: Mohammad Ekramol Islam Author-X-Name-First: Mohammad Ekramol Author-X-Name-Last: Islam Author-Name: Abdur Rashid Author-X-Name-First: Abdur Author-X-Name-Last: Rashid Title: (s, S) stochastic inventory system in Jackson network Abstract: In this work, we develop and analyse an (<i>s</i>, <i>S</i>) stochastic perishable inventory system at each node into Jackson network with a service facility in which the waiting hall of first queue has infinite capacity and second queue has finite capacity for the customers. Service times are exponentially distributed. We assume that demands arrive in the system according to a Poisson process with rate <i>&lambda;<SUB align=right><SMALL>i</SMALL></SUB></i>(> 0, &forall;<i>i</i>) and demands only single unit item at a time. The maximum storage capacity of the <i>i</i><SUP align=right><SMALL>th</SMALL></SUP> warehouse is fixed as <i>S<SUB align=right><SMALL>i</SMALL></SUB></i></i>; <i>i</i> = 1, 2. Whenever the inventory level reaches the reorder level <i>s<SUB align=right><SMALL>i</SMALL></SUB></i></i>(0 ≤ <i>s<SUB align=right><SMALL>i</SMALL></SUB></i></i> < <i>S<SUB align=right><SMALL>i</SMALL></SUB></i></i>), an order <i>Q<SUB align=right><SMALL>i</SMALL></SUB></i></i>(= <i>S<SUB align=right><SMALL>i</SMALL></SUB></i></i> – <i>s<SUB align=right><SMALL>i</SMALL></SUB></i></i>) units is placed to bring the level to <i>S<SUB align=right><SMALL>i</SMALL></SUB></i></i>. The lead-time is exponentially distributed. The items of inventory have exponential life times. The joint probability distribution of the number of customers in the system and the inventory level is obtained in the steady state case. Matrix analytical method is applied to solve for the steady state occupancy probabilities. Various system performance measures in the steady state are derived. A suitable cost function is defined and the long-run total expected cost rate is calculated. Sensitivity analysis has been carried out to study the effect of variation of parameters. Numerical examples and graphical illustrations are provided to illustrate the proposed model. Journal: Int. J. of Operational Research Pages: 416-436 Issue: 4 Volume: 43 Year: 2022 Keywords: Jackson network; (s, S)-policy; stability condition; performance analysis; sensitivity analysis. File-URL: http://www.inderscience.com/link.php?id=122813 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:4:p:416-436 Template-Type: ReDIF-Article 1.0 Author-Name: James P. Bailey Author-X-Name-First: James P. Author-X-Name-Last: Bailey Author-Name: Todd Easton Author-X-Name-First: Todd Author-X-Name-Last: Easton Author-Name: Fabio Vitor Author-X-Name-First: Fabio Author-X-Name-Last: Vitor Title: Octanary polyhedral branch and bound for integer programs Abstract: This paper introduces the octanary branching algorithm (OBA), a polyhedral branching technique to solve integer programs. Unlike the traditional branch and bound algorithm, each of OBA's branching nodes generates eight children instead of two. Four of them are created by equality constraints, while the other four use inequalities. This branching strategy allows a dimension reduction of the linear relaxation space of the four equality children, which should enable OBA to find quality integer solutions sooner than the branch and bound algorithm. Computational experiments showed that the branch and bound algorithm required over one billion nodes to identify a solution that is at least as good as the solution found by OBA after only half a million nodes. Consequently, OBA should replace the branch and bound algorithm during the first portion of the branching tree, be used to identify a warm start solution, or be implemented as a diving strategy. Journal: Int. J. of Operational Research Pages: 451-478 Issue: 4 Volume: 43 Year: 2022 Keywords: branch and bound; hyperplane branching; branching polyhedra; random diving; integer programming. File-URL: http://www.inderscience.com/link.php?id=122815 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:4:p:451-478 Template-Type: ReDIF-Article 1.0 Author-Name: Sourabh Bishnoi Author-X-Name-First: Sourabh Author-X-Name-Last: Bishnoi Author-Name: Girish Jain Author-X-Name-First: Girish Author-X-Name-Last: Jain Author-Name: Gokulananda Patel Author-X-Name-First: Gokulananda Author-X-Name-Last: Patel Title: Deriving weights of investment alternatives using DEAHP Abstract: Analytical hierarchy process (AHP) is known for its great flexibility and wide applicability. Because of ease of use, AHP has been studied extensively and used in all applications related to multi-criteria decision making (MCDM) since its inception. AHP converts qualitative opinion of experts into quantitative values and generates a comparison matrix. In this paper, multiple expert judgments have been considered for development of matrices for criteria and alternatives of an investment selection problem. Experts' opinion was sought on selection of four categories of investment avenues based on five different selection criteria. We have integrated data envelopment analysis (DEA) to generate local weights of criteria and alternatives of pairwise comparison matrices using fundamental scale developed by Saaty. Journal: Int. J. of Operational Research Pages: 362-377 Issue: 3 Volume: 45 Year: 2022 Keywords: data envelopment analysis; DEA; analytical hierarchy process; AHP; aggregation; linear programming; investment. File-URL: http://www.inderscience.com/link.php?id=127173 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:45:y:2022:i:3:p:362-377 Template-Type: ReDIF-Article 1.0 Author-Name: A. Azhagappan Author-X-Name-First: A. Author-X-Name-Last: Azhagappan Author-Name: T. Deepa Author-X-Name-First: T. Author-X-Name-Last: Deepa Title: Transient analysis of a Markovian N-policy queue with system disaster repair closedown setup times and control of admission Abstract: The main objective of this research work is to study the time-dependent behaviour of performance measures and probabilities for an <i>M</i> / <i>M</i> / 1 queueing model with some interesting parameters such as closedown, setup periods, disastrous breakdown of the system, repair, N-policy and different control mechanism for the arrivals when the server is under repair as well as busy. In order to reduce the cost of production and to increase the profit, the manufacturing industries follow a technique of not to start the service until the number of work pieces reaches a fixed threshold value. Shutting down the machines when no jobs are available and setting up before the commencement of service play significant contributions to reach the goals in business organisations. The probabilities of the model under consideration are derived by the method of generating function for the transient case. Some system performance measures and numerical simulations are also presented. Journal: Int. J. of Operational Research Pages: 279-291 Issue: 3 Volume: 44 Year: 2022 Keywords: N-policy queue; disaster and repair; closedown and setup times; control of admission; transient probabilities. File-URL: http://www.inderscience.com/link.php?id=124102 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:3:p:279-291 Template-Type: ReDIF-Article 1.0 Author-Name: Christopher Gaffney Author-X-Name-First: Christopher Author-X-Name-Last: Gaffney Title: Optimal deductible and coinsurance policies under mean-variance preferences Abstract: The paper presents a mean-variance analysis of optimal insurance coverage, showing how the relationship between the attitudes of the insured, in the form of their risk tolerance level, and the insurer, in the form of the insurance premium, affects insurance demand. Optimal parameter values (deductible, coverage limit, coinsurance level, and stop-loss limit) are derived, and policies which include coinsurance and either a stop-loss limit or a deductible reduce to a straight deductible policy in the optimum, are shown. Also shown was that straight coinsurance is inferior to these policies. Journal: Int. J. of Operational Research Pages: 349-359 Issue: 3 Volume: 44 Year: 2022 Keywords: mean variance; optimal deductible; coinsurance; stop-loss; risk aversion. File-URL: http://www.inderscience.com/link.php?id=124103 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:3:p:349-359 Template-Type: ReDIF-Article 1.0 Author-Name: Abdessamad Ait El Cadi Author-X-Name-First: Abdessamad Ait El Author-X-Name-Last: Cadi Author-Name: Nizar El Hachemi Author-X-Name-First: Nizar El Author-X-Name-Last: Hachemi Author-Name: Mohamed Anouar Jamali Author-X-Name-First: Mohamed Anouar Author-X-Name-Last: Jamali Author-Name: Abdelhakim Artiba Author-X-Name-First: Abdelhakim Author-X-Name-Last: Artiba Author-Name: Louis-Martin Rousseau Author-X-Name-First: Louis-Martin Author-X-Name-Last: Rousseau Title: An exact approach to the integration of non-cyclical preventive maintenance scheduling and production planning for a series-parallel production system Abstract: In this paper, we generalise a model for integrated non-cyclical preventive maintenance scheduling and production planning from a single machine to a series-parallel production line. As for a single machine, we consider a set of products that must be produced in lots during a given time horizon. The maintenance strategy involves possible preventive replacements at the beginning of each maintenance period and minimal repair at machine failure. The model, an integer linear program, determines the optimal production plan and preventive replacement for each machine of the production line. The objective is to minimise the total cost (preventive and corrective maintenance costs, setup costs, holding costs, backorder costs, and production costs) while meeting the demand for each product over the horizon. We performed experiments using CPLEX 12.5.1, and almost all instances were solved within five minutes with a reasonable gap. Journal: Int. J. of Operational Research Pages: 401-414 Issue: 3 Volume: 44 Year: 2022 Keywords: production planning; preventive maintenance; series-parallel production line; integer linear program; CPLEX. File-URL: http://www.inderscience.com/link.php?id=124104 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:3:p:401-414 Template-Type: ReDIF-Article 1.0 Author-Name: Amaria Ouis Khedim Author-X-Name-First: Amaria Ouis Author-X-Name-Last: Khedim Author-Name: Mehdi Souier Author-X-Name-First: Mehdi Author-X-Name-Last: Souier Author-Name: Zaki Sari Author-X-Name-First: Zaki Author-X-Name-Last: Sari Title: Combinatorial artificial bee colony algorithm hybridised with a new release of iterated local search for job shop scheduling problem Abstract: Job shop scheduling problem (JSP) is recognised as an attractive subject in production management and combinatorial optimisation. However, it is known as one of the most difficult scheduling problems. The present paper investigates the job shop scheduling problem in order to minimise the makespan with a new hybrid combinatorial artificial bee colony algorithm. Firstly, the proposed combinatorial version integrates a position based crossover for the updating of solutions and the rank-based selection for selecting solutions to be updated in the onlooker bees phase. Another purpose of this study consists to highlight the impact of its sequential hybridisation with a new release of iterated local search method called 'simple iterated local search (SILS)'. The proposed approaches are tested on many benchmark problems taken from the Operations Research Library (OR-Library). The simulation results show that the hybrid CABC performs the best in most of the studied cases. Journal: Int. J. of Operational Research Pages: 435-461 Issue: 4 Volume: 44 Year: 2022 Keywords: job shop scheduling problem; JSP; metaheuristics; artificial bee colony algorithm; iterated local search. File-URL: http://www.inderscience.com/link.php?id=125128 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:4:p:435-461 Template-Type: ReDIF-Article 1.0 Author-Name: Souhir Elleuch Author-X-Name-First: Souhir Author-X-Name-Last: Elleuch Author-Name: Bassem Jarboui Author-X-Name-First: Bassem Author-X-Name-Last: Jarboui Title: Improved memetic programming algorithm Abstract: Automatic programming is an efficient technique that has contributed to an important development in the field of artificial intelligence. Genetic programming (GP) is a well known automatic programming algorithm based on genetic algorithm and evolves programs. In the present paper, we propose a new automatic programming method called two-dimensional memetic programming. It combines GP with local searches. We also introduce a new program representation for automatic programming algorithms. For this reason, the memetic programming algorithm is extended to evolve this program specific structure. To show the effectiveness of our method, we tested it on benchmark problems drawn from time series prediction and medical datasets classification, and we compared it with the related techniques. Journal: Int. J. of Operational Research Pages: 389-400 Issue: 3 Volume: 44 Year: 2022 Keywords: genetic programming; memetic programming; local search; time-series forecasting; classification. File-URL: http://www.inderscience.com/link.php?id=124105 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:3:p:389-400 Template-Type: ReDIF-Article 1.0 Author-Name: Sandeep Handa Author-X-Name-First: Sandeep Author-X-Name-Last: Handa Author-Name: Tilak Raj Author-X-Name-First: Tilak Author-X-Name-Last: Raj Author-Name: Sandeep Grover Author-X-Name-First: Sandeep Author-X-Name-Last: Grover Title: An integrated approach for evaluating the enablers for green manufacturing using DEMATEL and analytic network process Abstract: In recent decades, increase in environmental awareness has motivated the manufacturers towards minimising the use of exhaustible resources. Green manufacturing focuses on manufacturing technologies and initiatives that optimise energy usage and resource conservation. Green manufacturing aims to minimise environmental impact of manufacturing activities. The central objective of green paradigm is the combination of economic and ecological efficiency. This study aims to identify the key enablers for green manufacturing. The study uses an integration of decision making trial and evaluation laboratory (DEMATEL) and analytic network process (ANP). The analysis reflects the interdependencies among the enablers of green manufacturing. The results indicates that customer demand, implementing eco innovation and availability of collaborative suppliers are the top three enablers for the transition towards green manufacturing. Journal: Int. J. of Operational Research Pages: 415-434 Issue: 4 Volume: 44 Year: 2022 Keywords: green manufacturing; enablers; DEMATEL; analytic network process; ANP. File-URL: http://www.inderscience.com/link.php?id=125129 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:4:p:415-434 Template-Type: ReDIF-Article 1.0 Author-Name: Osmar Martín Salvador-Grijalva Author-X-Name-First: Osmar Martín Author-X-Name-Last: Salvador-Grijalva Author-Name: Sonia Valeria Avilés-Sacoto Author-X-Name-First: Sonia Valeria Author-X-Name-Last: Avilés-Sacoto Author-Name: Galo Eduardo Mosquera-Recalde Author-X-Name-First: Galo Eduardo Author-X-Name-Last: Mosquera-Recalde Title: Application of a mobile facility routing problem in a delivery company Abstract: In the last years, the growth of e-commerce has caused several changes in the way of how companies administrate their facilities and the transportation of their vehicles. Nowadays, companies must deal with small orders, short delivery schedules and a variable workload. This paper proposes a methodology, based the Ecuadorian company ZDelivery, to optimise the delivery of products with the use of mobile facilities. A series of tools are employed to solve the problem. In first place, a clustering technique is used to find strategic points within the city where the mobile facilities can be located. Then, an adaptation of a mobile facility routing problem is applied to determine the location and time where the mobile facilities should be located. Finally, to solve the model, the Monte Carlo method is applied to handle the uncertainty of the demand. This methodology applied to ZDelivery gives an optimal route for the fleet of the mobile facilities of the company between Friday's nights and Saturday's mornings where the strategic points in which the mobile facilities need to be located are specified for each mobile facility in intervals of 15 minutes. Journal: Int. J. of Operational Research Pages: 496-521 Issue: 4 Volume: 44 Year: 2022 Keywords: mobile facility; routing problem; Monte Carlo simulation; delivery; clustering; transportation; vehicles; demand; strategic points; time window; costs. File-URL: http://www.inderscience.com/link.php?id=125130 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:4:p:496-521 Template-Type: ReDIF-Article 1.0 Author-Name: Muruganantham Sowmiya Author-X-Name-First: Muruganantham Author-X-Name-Last: Sowmiya Author-Name: B. Banu Rekha Author-X-Name-First: B. Banu Author-X-Name-Last: Rekha Author-Name: Elangeeran Malar Author-X-Name-First: Elangeeran Author-X-Name-Last: Malar Author-Name: K.R. Ashwin Kumaran Author-X-Name-First: K.R. Ashwin Author-X-Name-Last: Kumaran Title: Hierarchical learning model for early prediction of coronary artery atherosclerosis Abstract: Artificial intelligence plays an ever-increasing role in developing human-like intelligent machines. In the modern world, physical activities in which people indulge have reduced and this has made them prone to heart diseases such as coronary artery disease (CAD). Coronary artery atherosclerosis (CAA) is one of the main causes of CAD and therefore early prediction of CAA is indispensable to prevent the risk of people getting affected by CAD. This work presents the machine learning model which provides more information on the exceptional cases while retaining the existing traditional classifier for early prediction of CAA. The proposed model performs outliner detection using local outlier factor (LOF) and class balancing using synthetic minority oversampling technique. Genetic algorithm is used for prominent feature selection and utilises support vector machine and neural network as the classifier. Two datasets namely UCI dataset and South African heart disease dataset are used to implement the model. Results show that the proposed model gives better accuracy for the above datasets along with the traditional methods. Journal: Int. J. of Operational Research Pages: 473-495 Issue: 4 Volume: 44 Year: 2022 Keywords: machine learning; support vector machine; SVM; neural network; local outlier factor; LOF; feature selection. File-URL: http://www.inderscience.com/link.php?id=125131 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:4:p:473-495 Template-Type: ReDIF-Article 1.0 Author-Name: Deepak Gupta Author-X-Name-First: Deepak Author-X-Name-Last: Gupta Author-Name: Sonia Goel Author-X-Name-First: Sonia Author-X-Name-Last: Goel Title: Branch and bound technique for two stage flow shop scheduling model with equipotential machines at every stage Abstract: Among the different jobs in manufacturing industries job scheduling is one of the most significant at the performance judgment level to facilitate organisation to attain competitiveness. Scheduling consist in allotment of limited resources to jobs over time in order to achieve one or more optimisation objectives. Scheduling has been mostly investigated in case of single machine at each stage or several machine at one stage and sole machine at rest of the stages. This study proposes an exact branch and bound algorithm to schedule n-jobs on two machines with parallel equipotential machines at every phase. The purpose of this work is to find best possible schedule of jobs with the intention to diminish the entire elapsed time and allocate in an optimal mode the lead time of the jobs to parallel equipotential machines of both the type so that total cost of doing all the jobs is minimised. Journal: Int. J. of Operational Research Pages: 462-472 Issue: 4 Volume: 44 Year: 2022 Keywords: scheduling; elapsed time; equipotential machines; operating cost. File-URL: http://www.inderscience.com/link.php?id=125132 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:4:p:462-472 Template-Type: ReDIF-Article 1.0 Author-Name: Zhiyi Zhuo Author-X-Name-First: Zhiyi Author-X-Name-Last: Zhuo Title: Research on optimal product supply strategies for manufacturer-to-group customer under a real demand pattern Abstract: In a product supply chain, customer demand determines the market and supply as well as the benefits accruing to manufacturers and retailers. Customer demand has three different patterns: real, false, and semi-real. This paper develops a mathematical model to investigate the factors that determine manufacturers' design and plan of product supply strategies for group customers under the real demand pattern and solve for maximum profit. We use numerical examples to verify the validity of the model. This paper's contribution is the construction of two mathematical models for off-invoice mode and unsold recycling mode of manufacturer-to-group customer product supply strategies under a real demand pattern. Journal: Int. J. of Operational Research Pages: 550-561 Issue: 4 Volume: 44 Year: 2022 Keywords: real demand pattern; product supply strategies; profit function; optimal method. File-URL: http://www.inderscience.com/link.php?id=125133 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:4:p:550-561 Template-Type: ReDIF-Article 1.0 Author-Name: Puja Bharti Author-X-Name-First: Puja Author-X-Name-Last: Bharti Author-Name: Sushma Jain Author-X-Name-First: Sushma Author-X-Name-Last: Jain Title: State-of-the-art in optimisation and heuristics to solve manufacturing scheduling problem Abstract: Manufacturing scheduling is known to be one of the most complex optimisation problems and falls in the category of NP-hard problems. Continuous efforts have been made by the researchers in the past to find convincingly accurate solutions for the instances in a reasonable time. It is valuable to compile the abundant literature available in this area for better understanding as well as convenience. This survey presents a systematic review of the optimisation approaches to solve manufacturing scheduling problem. Primarily, the research published during the period 2001-March 2019 has been considered. For this, a total of 456 research papers were examined. A comprehensive, well-informed examination and realistic analysis of the available literature provides an insight into major developments that has taken place pertaining to the use of heuristics/meta-heuristics in solving this problem. A classification based on objectives, optimisation techniques, benchmark instances, software tools, etc., highlights the research trends in this field along with future directions. Journal: Int. J. of Operational Research Pages: 292-348 Issue: 3 Volume: 44 Year: 2022 Keywords: optimisation; heuristics; NP-hard; job shop scheduling; review; meta-heuristics; state-of-art; single objective; multi-objective; multi-objective evolutionary algorithms; MOEAs; manufacturing scheduling. File-URL: http://www.inderscience.com/link.php?id=124110 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:3:p:292-348 Template-Type: ReDIF-Article 1.0 Author-Name: Kolsoom Zamani Author-X-Name-First: Kolsoom Author-X-Name-Last: Zamani Author-Name: Hashem Omrani Author-X-Name-First: Hashem Author-X-Name-Last: Omrani Title: A complete information PCA-imprecise DEA approach for constructing composite indicator with interval data: an application for finding development degree of cities Abstract: Composite indicator approach is widely used for finding development degree of regions. One of the most important models for constructing composite indicator is data envelopment analysis (DEA) model. This paper presents a complete information principal component analysis (CIPCA)-imprecise DEA (IDEA) approach for finding development degree of cities with uncertain data. CIPCA is applied to reduce the number of indicators. The output of the CIPCA is a set of new indicators with lower and upper bounds. These indicators are considered as indicators of IDEA and final ranks of cities are calculated by IDEA model. To illustrate the capability of CIPCA-IDEA approach, the development degrees of cities in Kurdistan province of Iran are calculated. First, 62 development indicators are selected and the related interval data for year 2015 are gathered. Then, the proposed approach is applied for nine cities of Kurdistan province and development degree for each city is finally calculated. The results indicate that in the overall ranking, Bijar City is ranked first in the development and Baneh is ranked as ninth city. Journal: Int. J. of Operational Research Pages: 522-549 Issue: 4 Volume: 44 Year: 2022 Keywords: composite indicator; CIPCA; imprecise data envelopment analysis; IDEA; development degree. File-URL: http://www.inderscience.com/link.php?id=125134 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:4:p:522-549 Template-Type: ReDIF-Article 1.0 Author-Name: Chandra Prakash Garg Author-X-Name-First: Chandra Prakash Author-X-Name-Last: Garg Title: Modelling and optimisation of reverse logistics network under fuzzy environment Abstract: Resource recovery, environmental protection, sustainable production and consumption have got much attention in recent times because of growing ecological and social concern, economic issues and government regulations. In order to recover products, which include reuse, refurbishing, recycling and proper disposal of wastes that can help reduce their impact on environment, human and society, demands an effective reverse logistics (RL) network structure. In this paper, multi-product, multi-facility and multi-stage RL network is designed under uncertain environment and optimisation of the proposed network is analysed by fuzzy mixed integer linear programming (MILP) model with the aim of optimising total RL profit, quantity flows and facility locations. The proposed model is further supported by a numerical example of electronics products. Subsequently, the robustness of the model verifies the balanced relationship between the degree of feasibility and the degree of satisfaction of the management. Journal: Int. J. of Operational Research Pages: 360-388 Issue: 3 Volume: 44 Year: 2022 Keywords: reverse logistics network; mixed integer linear programming; MILP; fuzzy; electronics products. File-URL: http://www.inderscience.com/link.php?id=124111 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:44:y:2022:i:3:p:360-388 Template-Type: ReDIF-Article 1.0 Author-Name: M.S. Kagthara Author-X-Name-First: M.S. Author-X-Name-Last: Kagthara Author-Name: M.G. Bhatt Author-X-Name-First: M.G. Author-X-Name-Last: Bhatt Title: Development of maze puzzle algorithm for the job shop scheduling Abstract: Maze puzzle concept has been introduced for solving job shop scheduling problem. Maze puzzle algorithm (MPA) is based on rotation and random jumping which explores the solution space as well as exploits the solution near to optimum. Coding is done using MATLAB software, and benchmark problem is evaluated for assessing efficiency of the algorithm. The algorithm can be used for optimisation of makespan for the given problem. The results are compared with other methods like GA, SA, SBI, SBII, PSO, BBO and TS, and found better than GA, SA, SBI, PSO, BBO but poor than SB-2 and TS. Journal: Int. J. of Operational Research Pages: 255-270 Issue: 3 Volume: 43 Year: 2022 Keywords: maze puzzle; optimisation; job shop scheduling; makespan; MATLAB; jumping; rotation. File-URL: http://www.inderscience.com/link.php?id=122333 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:3:p:255-270 Template-Type: ReDIF-Article 1.0 Author-Name: Sarah Talal Khrais Author-X-Name-First: Sarah Talal Author-X-Name-Last: Khrais Author-Name: Saleh Fahed Saleh Alkhatib Author-X-Name-First: Saleh Fahed Saleh Author-X-Name-Last: Alkhatib Title: Project management best practices and project success in developing economies - a Jordanian study Abstract: This study aims to identify project management best practices, their impact relationships and their role in construction projects success in developing economies. Based on project experts' semi-structured interviews, several project management best practices have been identified and validated. First, the DEMATEL technique is used to analyse the impact relationships between these practices and to classify them into 'cause' and 'effect' factors. Then, data from 537 project managers, engineers and administrative workers have been collected, coded and analysed. All participants agreed on and confirmed the importance of the identified practices. But, the importance level of these practices is not the same. Specifically, there are significant statistical correlations between the cause and effect practices and success indicators; there are significance statistical differences on participants' perception towards best practices and success factors in terms of gender, age, educational level, specialisation, experience in current job and experience in general. Finally, several recommendations about the construction sector were addressed. Journal: Int. J. of Operational Research Pages: 360-389 Issue: 3 Volume: 43 Year: 2022 Keywords: project management best practices; construction projects; project success factors; developing economies projects; DEMATEL technique; Jordan. File-URL: http://www.inderscience.com/link.php?id=122334 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:3:p:360-389 Template-Type: ReDIF-Article 1.0 Author-Name: Somayeh Khezri Author-X-Name-First: Somayeh Author-X-Name-Last: Khezri Author-Name: Gholam Reza Jahanshahloo Author-X-Name-First: Gholam Reza Author-X-Name-Last: Jahanshahloo Author-Name: Akram Dehnokhalaji Author-X-Name-First: Akram Author-X-Name-Last: Dehnokhalaji Author-Name: Farhad Hosseinzadeh Lotfi Author-X-Name-First: Farhad Hosseinzadeh Author-X-Name-Last: Lotfi Title: A complete ranking of decision making units with interval data Abstract: Interval data envelopment analysis (interval DEA) deals with the problem of efficiency assessment when the inputs and/or outputs of decision making units (DMUs) are given as interval data. This paper focuses on the problem of ranking DMUs with interval data. First, we define extreme efficient units, super efficiency score, the best and the worst efficiency (inefficiency) frontiers in the interval DEA context. Then, we propose a novel method based on the lower and upper super efficiency scores of a unit under evaluation and the distance of that unit to four developed frontiers. Our method ranks all efficient and inefficient units which is one of the main advantages of it. Our method uses several essential criteria simultaneously to rank units with interval data. These criteria increase the discrimination power of our proposed method. Potential application of this method is illustrated with a dataset consisting of 30 branches of the social security insurance organisation in Tehran. Journal: Int. J. of Operational Research Pages: 332-359 Issue: 3 Volume: 43 Year: 2022 Keywords: date envelopment analysis; DEA; interval DEA; decision making unit; DMU; ranking. File-URL: http://www.inderscience.com/link.php?id=122335 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:3:p:332-359 Template-Type: ReDIF-Article 1.0 Author-Name: Anushri Maji Author-X-Name-First: Anushri Author-X-Name-Last: Maji Author-Name: Asoke Kumar Bhunia Author-X-Name-First: Asoke Kumar Author-X-Name-Last: Bhunia Author-Name: Shyamal Kumar Mondal Author-X-Name-First: Shyamal Kumar Author-X-Name-Last: Mondal Title: Reliability optimisation of parallel-series system with interval valued and fuzzy environment via GA Abstract: Reliability is an essential tool for a system. In this paper, we have considered a reliability optimisation problem in parallel-series system. Here, we have discussed about that how many components are needed to maximise the system reliability with some resource constraints such as cost, weight, volume, etc. Also, to get more relaxation we have assumed that the component reliabilities are interval valued number, lie between 0 and 1. Here, the constraint coefficients have been taken in fuzzy environment. Also, the fuzzy constraints have been defuzzified using possibility and necessity measures. The interval valued system reliability has been reduced to precise form applying centre-radius method. After reduction, our problem has been converted to a multi objective reliability optimisation problem with cost, volume, weight etc. as constraints. Finally, the proposed model has been illustrated numerically to study the feasibility of the system considering a real life example which has been solved by multi-objective genetic algorithm (MOGA). Journal: Int. J. of Operational Research Pages: 271-298 Issue: 3 Volume: 43 Year: 2022 Keywords: parallel-series system; interval valued component reliability; system reliability; fuzzy constraint coefficients; genetic algorithm. File-URL: http://www.inderscience.com/link.php?id=122336 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:3:p:271-298 Template-Type: ReDIF-Article 1.0 Author-Name: Anuradha Sahoo Author-X-Name-First: Anuradha Author-X-Name-Last: Sahoo Author-Name: Jayanta Kumar Dash Author-X-Name-First: Jayanta Kumar Author-X-Name-Last: Dash Title: Solving a single period inventory model with fuzzy inequality Abstract: The purpose of this paper is to present a fuzzy chance-constrained single period inventory model (FCCSPIM) in which the fuzziness appears in the space constraint and objective function is crisp. Here the partial order relation exists in between a random variable and a real number. That means the probability of the event is discussed under vague data. Our approach for the solution process uses mostly fuzzy Zimmermann technique to convert the FCCSPIM into a proper deterministic equivalent. Then the resulting nonlinear deterministic model is solved by using LINGO software. The result indicate that the fuzzy programming approach is effective for the inventory problem. The applications of an optimisation model under uncertainty are used to solve day to day problems. Many methods were developed by using tools of mathematics, probability theory and stochastic process. Here, one new approach of fuzzy programming technique is introduced to obtain a deterministic form. Journal: Int. J. of Operational Research Pages: 318-331 Issue: 3 Volume: 43 Year: 2022 Keywords: single period inventory model; SPIM; chance constrained programming problem; fuzzy partial order relation. File-URL: http://www.inderscience.com/link.php?id=122337 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:3:p:318-331 Template-Type: ReDIF-Article 1.0 Author-Name: Jon Lerche Author-X-Name-First: Jon Author-X-Name-Last: Lerche Author-Name: Hasse Neve Author-X-Name-First: Hasse Author-X-Name-Last: Neve Author-Name: Søren Wandahl Author-X-Name-First: Søren Author-X-Name-Last: Wandahl Author-Name: Allan Gross Author-X-Name-First: Allan Author-X-Name-Last: Gross Title: Categorisation of the offshore wind production system Abstract: This paper investigates and describes the production system characteristics for offshore wind turbine assembly and compares it to manufacturing and construction. It requires understanding of the organisations, products and processes to categorise the production system for offshore wind farm assembly. This study compares explorative cases with literature describing the production system characteristics from production and manufacturing domains. The categorisation is completed by comparing product-process first and then product-organisation. To understand the identified differences, the results are displayed in matrixes with manufacturing and construction characteristics from the literature, compared with field notes, observations and archived data from wind turbine assembly cases. The results show offshore wind turbine assembly as a hybrid production system in this comparative study. The comparison contributes to the novel understanding of the wind industry and is theory building within operations research. It establishes a baseline for further operational research within the offshore wind domain. Journal: Int. J. of Operational Research Pages: 299-317 Issue: 3 Volume: 43 Year: 2022 Keywords: construction; comparison; lean; offshore wind; operations management; production system theory. File-URL: http://www.inderscience.com/link.php?id=122338 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:3:p:299-317 Template-Type: ReDIF-Article 1.0 Author-Name: Dharmendra K. Yadav Author-X-Name-First: Dharmendra K. Author-X-Name-Last: Yadav Author-Name: Dinesh K. Sharma Author-X-Name-First: Dinesh K. Author-X-Name-Last: Sharma Author-Name: S.K. Yadav Author-X-Name-First: S.K. Author-X-Name-Last: Yadav Title: A new generalised median-based estimator of the finite population mean 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. 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. Journal: Int. J. of Operational Research Pages: 498-511 Issue: 4 Volume: 43 Year: 2022 Keywords: median; ratio estimator; bias; MSE; efficiency. File-URL: http://www.inderscience.com/link.php?id=122863 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:4:p:498-511 Template-Type: ReDIF-Article 1.0 Author-Name: Mahboubeh Norouzi Author-X-Name-First: Mahboubeh Author-X-Name-Last: Norouzi Author-Name: Arezoo Atighehchian Author-X-Name-First: Arezoo Author-X-Name-Last: Atighehchian Author-Name: Saeedeh Ketabi Author-X-Name-First: Saeedeh Author-X-Name-Last: Ketabi Title: Robust operating room planning through a novel modified block scheduling strategy Abstract: In this article, the surgical case assignment problem (SCAP) with uncertain duration of surgeries is assessed. This problem is defined as assigning the subsets of patients on the waiting list to the time blocks of operating rooms (ORs) in a given planning horizon. To further increase the OR utility rate and service rate, a novel <i>modified block scheduling strategy</i> is proposed and modelled as a mixed integer programming model. Then a robust optimisation model is proposed to tackle uncertainties in surgery duration. A set of real-based instances from 'Al Zahra Hospital', a teaching hospital in Iran, is applied to verify the proposed deterministic model. The optimal solution is compared with the actual hospital plan indicating the efficiency of this proposed model in practice. A robust model is evaluated through a set of instances. Numerical results indicate that the average percent of overtime reduction is 51.95% by applying the robust model. Journal: Int. J. of Operational Research Pages: 479-497 Issue: 4 Volume: 43 Year: 2022 Keywords: operating room planning; robust optimisation; modified block scheduling; mathematical programming. File-URL: http://www.inderscience.com/link.php?id=122868 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijores:v:43:y:2022:i:4:p:479-497