A collaborative humanitarian relief chain design for disaster response. (October 2022)

• Record Type: Journal Article
• Authors: Shokr, Iman; Jolai, Fariborz; Bozorgi-Amiri, Ali
• Is Part Of: Computers & industrial engineering. Volume 172:Part A(2022)
• Publication Date: 2022-10
• Subjects: Humanitarian relief chain -- Humanitarian organizations -- Collaboration -- Disaster -- Resource-sharing -- Lagrangian relaxation; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108643
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Proposing a novel collaborative humanitarian relief chain design model. Utilizing information- and resource-sharing as coordination mechanisms. Developing a Lagrangian relaxation algorithm for solving large-scale problems. Dealing with disruption risks by a scenario-based robust optimization approach. Presenting a case study to represent the applicability of the proposed model. Abstract: Pre- and post-disaster relief operations often involve several humanitarian organizations (HOs) working simultaneously. Balanced and systemic resource-sharing schemes may enable HOs to collaborate effectively and conduct efficient relief operations. In this study, a novel two-stage stochastic model is developed which depends on information- and resource-sharing as coordination mechanisms to ensure proper relief operations while multiple HOs exist in the relief chain. A collaborative humanitarian relief chain (CHRC) consisting of several HOs is designed to make the required decisions on the ways in which relief items are procured, pre-positioned, and distributed pre- and post-disaster. The model is also tweaked to handle potential risks of disruption by an efficient approach. Furthermore, a Lagrangian relaxation algorithm is developed to solve large-scale problems. The performance of the proposed model is tested by being applied to a real-world disaster, namely the 2017 Kermanshah earthquake, in Iran. Several sensitivity analyses are performed to evaluate the applicability of the model and compare the performance of the collaborative decision-making approach proposed in this study to decentralized approaches. Lastly, a number of managerial insights are drawn from the findings.