Humanitarian supply chain management: modeling the pre and post-disaster relief operations

Abstract

Purpose This study proposed a mathematical model for decision-making in the pre- and post-disaster phases. This research aims to develop a mathematical model for three important fields in the context of humanitarian logistics; stock prepositioning, facility location and evacuation planning in the humanitarian supply chain (HSC) network design. Design/methodology/approach This study applied three optimization techniques; classical approach (CA), pattern search algorithm (PSA) and Genetic Algorithm (GA) to solve the proposed mathematical model. The proposed mathematical model attempts to minimize the total relief items supply chain cost and evacuation chain cost of the HSC. A real case study of cyclone Fani, 2019 in Orissa, India is applied to validate the proposed mathematical model and to show the performance of the model. Findings The results demonstrate that heuristic approach; PSA performs better and optimal solutions are obtained in almost all the cases as compared to the GA and CA. Research limitations/implications This study is limited to deterministic demands in the affected regions, and different scenarios of the disaster events are not considered. Social implications The finding reveals that the proposed model can help the humanitarian stakeholders in making decisions on facility location, relief distribution and evacuation planning in disaster relief operations. Originality/value The results of this study may offer managerial insights to practitioners and humanitarian logisticians who are engaged in HSC implementation.

Quick-Response Model for Pre- and Post-Disaster Evacuation and Aid Distribution: The Case of the Tula River Flood Event

“…Concerning both the pre-disaster and post-disaster phases, problems related to stock prepositioning, facility placement, evacuation planning, and hospital and distribution center locations have been addressed. These complex challenges have been approached using various metaheuristics, including classical approaches (CAs), pattern search algorithms (PSAs), genetic algorithms (GAs), and Non-Dominated Sorting Genetic Algorithm III [15,[28][29][30].…”