Risk-averse optimization of disaster relief facility location and vehicle routing under stochastic demand

Shaopeng Zhong, Rong Cheng, Yu Jiang, Zhong Wang, Allan Larsen and Otto Anker Nielsen

Transportation Research Part E: Logistics and Transportation Review, 2020, vol. 141, issue C

Abstract: Disasters such as fires, earthquakes, and floods cause severe casualties and enormous economic losses. One effective method to reduce these losses is to construct a disaster relief network to deliver disaster supplies as quickly as possible. This method requires solutions to the following problems. 1) Given the established distribution centers, which center(s) should be open after a disaster? 2) Given a set of vehicles, how should these vehicles be assigned to each open distribution center? 3) How can the vehicles be routed from the open distribution center(s) to demand points as efficiently as possible? 4) How many supplies can be delivered to each demand point on the condition that the relief allocation plan is made a priority before the actual demands are realized? This study proposes a model for risk-averse optimization of disaster relief facility location and vehicle routing under stochastic demand that solves the four problems simultaneously.

Keywords: Disaster relief location-routing problem; Conditional value at risk with regret; Bi-objective optimization; Nash bargaining solution; Hybrid genetic algorithm (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (22)

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DOI: 10.1016/j.tre.2020.102015

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