Multi-level facility location-allocation problem for post-disaster humanitarian relief distribution

Seyed Mahdi Shavarani (Alliance Manchester Business School, University of Manchester, Manchester, UK)

Journal of Humanitarian Logistics and Supply Chain Management

ISSN: 2042-6747

Publication date: 30 April 2019

Abstract

Purpose

Previously use of drones as a relief distribution vehicle was studied in several studies where required number of drones and the best locations for the relief centers were investigated. The maximum travel distance of drones without a need to recharge is limited by their endurance. Recharge stations can be used to extend the coverage area of the drones. The purpose of this paper is to find the best topology for both relief centers and recharge stations to cover a large-scale area with minimum and feasible incurred costs and waiting times.

Design/methodology/approach

A multi-level facility location problem (FLP) is utilized to find the optimum number of relief centers and refuel stations and their locations. It is supposed that the demand occurs according to Poisson distribution. The allocation of the demand is based on nearest neighborhood method. A hybrid genetic algorithm is proposed to solve the model. The performance of the algorithm is examined through a case study.

Findings

The proposed method delivers increased efficiency and responsiveness of the humanitarian relief system. The coverage area of the drones is extended by refuel stations, total costs of the system are reduced and the time to respond an emergency, which is an important factor in survival rate, is significantly decreased.

Originality/value

This study proposes a multi-level FLP to simultaneously account for recharge stations, relief centers and the number of required drones to cover all the demand for relief in a post-disaster period.

Keywords

Citation

Shavarani, S. (2019), "Multi-level facility location-allocation problem for post-disaster humanitarian relief distribution", Journal of Humanitarian Logistics and Supply Chain Management, Vol. 9 No. 1, pp. 70-81. https://doi.org/10.1108/JHLSCM-05-2018-0036

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Publisher

:

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

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