A novel two-stage stochastic programming model to design an integrated disaster relief supply chain network-a case study

IF 6.9 2区 管理学 Q1 MANAGEMENT
Leyla Fazli
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Abstract

When a disaster strikes, there is always a demand for life-supporting commodities, whose slow and ineffective delivery can result in huge human and financial losses. Warehouse location and the storage of necessary relief commodities (RCs) before a disaster, and the proper distribution of RCs among affected people following a disaster can improve performance and reduce latency when responding to a given disaster. Hence, many researchers have focused on these fields while overlooking some crucial actual conditions as a result of the complexity of the problem. Consequently, this study develops a location-inventory-distribution problem in disaster relief supply chain (DRSC) considering the gradual injection of the limited pre-disaster budgets, the time value of money, and various evaluation criteria for locating warehouses. In this regard, a novel multi-objective two-stage scenario-based stochastic programming model under a pre-disaster multi-period planning time horizon (PTH) is presented. In each period, pre-disaster warehouse location and inventory management are addressed in the first stage, and the post-disaster distribution of the stocked RCs is planned in the second stage. Utilizing new priority-weighted service utility and balance measures, the model strives to optimize deprivation cost, demand coverage, and fair service. The maximization of warehouses’ utility is done according to various criteria and using a data envelopment analysis (DEA) model integrated with the model. The applicability and performance of the model are validated via a real-world case study followed by various tests and sensitivity analyses. The outcomes show that the model significantly improves logistics and deprivation costs, satisfied demands, fair service, and warehouses’ utility.

Abstract Image

设计综合救灾供应链网络的新型两阶段随机编程模型--案例研究
当灾难发生时,人们总是需要维持生命的商品,而这些商品的缓慢和无效运送会造成巨大的人员和经济损失。灾害发生前的仓库选址和必要救灾物资(RCs)的存储,以及灾害发生后向灾民适当分发 RCs,都可以提高救灾效率,减少延迟。因此,许多研究人员都将注意力集中在这些领域,但由于问题的复杂性而忽略了一些关键的实际条件。因此,考虑到灾前有限预算的逐步注入、资金的时间价值以及仓库选址的各种评估标准,本研究提出了救灾供应链(DRSC)中的选址-库存-分配问题。为此,本文提出了一种基于灾前多期规划时间跨度(PTH)的新型多目标两阶段情景随机编程模型。在每个时期,第一阶段解决灾前仓库选址和库存管理问题,第二阶段规划灾后库存 RC 的分配。利用新的优先权加权服务效用和平衡度量,该模型努力优化匮乏成本、需求覆盖率和公平服务。仓库效用的最大化是根据各种标准并利用与模型集成的数据包络分析(DEA)模型实现的。该模型的适用性和性能通过实际案例研究以及各种测试和敏感性分析进行了验证。结果表明,该模型极大地改善了物流和匮乏成本、满足需求、公平服务和仓库效用。
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来源期刊
CiteScore
6.20
自引率
23.30%
发文量
104
期刊介绍: Operations Management Research is a peer-reviewed journal that focuses on rapidly publishing high-quality research in the field of operations management. It aims to advance both the theory and practice of operations management across a wide range of topics and research paradigms. The journal covers all aspects of operations management, including manufacturing, supply chain, health care, and service operations. It welcomes various research methodologies, such as case studies, action research, surveys, mathematical modeling, and simulation. The goal of Operations Management Research is to promote research that enhances both the theory and practice of operations management, as it is an applied discipline. The journal also publishes Academic Notes, which are special papers that address research methodologies, the direction of the operations management field, and other topics of interest to academicians. Additionally, there is a demand for shorter and more focused research articles in operations management, which this journal aims to fulfill.
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