A scenario-based game theory integrating with a location-allocation-routing problem in a pre- and post-disaster humanitarian logistics network under uncertainty

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2024-04-16 DOI:10.1108/jm2-08-2023-0181
Ali Beiki Ashkezari, Mahsa Zokaee, Erfan Rabbani, Masoud Rabbani, Amir Aghsami
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Abstract

Purpose

Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

Design/methodology/approach

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Findings

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

Originality/value

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

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基于情景的博弈论与不确定情况下灾前和灾后人道主义物流网络中的位置分配-路线问题相结合
目的预先放置和分发救灾物品是灾害管理的重要组成部分,因为它同时考虑了灾前和灾后两个阶段的活动。本研究旨在通过一个新颖的数学模型来解决这一问题。在本研究中,开发了一个双目标混合整数线性规划模型来解决救灾物资的预置和分发问题,并将其表述为一个具有不确定参数的位置-分配-路由综合问题。人道主义供应链由救援设施(RFs)和需求点(DPs)组成。考虑了易腐和不易腐救灾商品 (RC)、不同类型的车辆、不同的运输模式、交付易腐商品的时间窗口以及未满足需求的发生。应用基于情景的博弈论从不同供应商处采购 RC,并通过与理想解技术的相似性,采用综合最佳-最差法-偏好排序技术来确定 DPs 的重要性。为了验证该模型,以 2019 年伊朗洪水为案例进行了研究,并提供了有用的管理见解。研究结果管理人员可以有效地调整他们对响应时间和网络总成本的偏好,并在决策中使用敏感性分析结果。独创性/价值该模型在灾前阶段确定RFs的位置,将DPs分配到RFs,并在灾后阶段确定RCs从RFs到DPs的路径,从而使人道主义物流网络的总成本和响应时间最小化。
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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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