Designing a sustainable humanitarian relief logistics model in pre- and postdisaster management

• Published in: International journal of sustainable transportation Vol. 15; no. 8; pp. 604 - 620
• Main Authors: Boostani, Abtin, Jolai, Fariborz, Bozorgi-Amiri, Ali
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 08.06.2021
• Subjects: Compromise programing technique; humanitarian logistics; lexicographic optimization method; multi-objective mixed integer stochastic programing; sustainability
• ISSN: 1556-8318; 1556-8334
• DOI: 10.1080/15568318.2020.1773975

In this study, a three-level relief chain problem is proposed in pre- and postdisaster phases in order to support sustainability in the context of humanitarian relief operations. We develop a comprehensive relief logistics network considering the strategic and tactical planning issues of facility location, procurement, and resource allocation, and including multi-objective mixed-integer stochastic programing model which minimizes total costs of the humanitarian relief supply chain (the costs of the preparedness and response phases), maximizes the social welfare (through maximizing the minimum satisfaction rates among the disaster areas), and minimizes the environmental impacts (ecological impacts of the packaging of relief goods, and CO 2 emissions in transportation routes of the proposed network in pre- and postdisaster phases), simultaneously. The proposed multi-objective sustainable humanitarian relief logistics problem (SHRLP) is solved using the Compromise Programing (CP) technique and Lexicographic Optimization Method (LOM). Two numerical examples with different scales are provided based on surveying data of possible Tehran earthquakes. The results of solving numerical examples via CP and LOM methods are presented and compared. The efficient Pareto optimal solutions are also provided to examine different preferences of the objectives. LOM produces better solutions than the CP technique in terms of the satisfaction rates and fair distribution of relief items. Also, the results indicate that the novel model is able to survey and model both the sustainability approach and the uncertainty of a disaster relief condition in support of more efficient and effective plans for sustainable facility locations, and procurement and delivery of reliefs.