P-facility capacitated location problem with customer-equilibrium decisions: a recreational case study in Mazandaran province

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2024-05-27 DOI:10.1108/jm2-08-2023-0194
M. Zaferanieh, Mahmood Sadra, Toktam Basirat
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Abstract

Purpose This paper aims to propose a bi-level mixed integer linear location-allocation problem. The upper-level objective function is dedicated to minimizing the total distances covered by customers to meet the p-selected facilities and the fixed cost values for establishing these facilities. While in the lower level, a customer preference function evaluates the priority of customers in selecting facilities. Design/methodology/approach The solution approach to the proposed model uses the Karush–Kuhn–Tucker (KKT) optimality conditions to the lower-level problem where a set of p-selected facilities are introduced as the selection of the upper-level decision maker. The bi-level model reduces to a single-level model with some added binary variables. Findings Sensitivity analysis of the proposed bi-level model concerning variations of such different parameters as customers’ preferences and the number of selected facilities have been provided, using some numerical examples. Also, locating a recreational facility in Mazandaran province, Iran, has been provided to evaluate the reliability of the proposed model and efficiency of the solution approach, as well. Originality/value To the best of the authors’ knowledge, this paper is original and its findings are not available elsewhere.
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具有客户均衡决策的 P 型设施容量选址问题:马赞达兰省的娱乐案例研究
本文旨在提出一个双级混合整数线性位置分配问题。上层目标函数致力于最小化客户为满足 p 个选定设施所需的总距离以及建立这些设施的固定成本值。设计/方法/途径该建议模型的求解方法使用了 Karush-Kuhn-Tucker(KKT)优化条件来解决下层问题,其中引入了一组 p 个选定的设施作为上层决策者的选择。研究结果通过一些数字示例,对所提出的双层模型进行了灵敏度分析,涉及客户偏好和所选设施数量等不同参数的变化。此外,还提供了伊朗马赞达兰省一个娱乐设施的定位情况,以评估所提出模型的可靠性和解决方法的效率。
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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
期刊介绍: Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.
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