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

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.