Citation

Wang Z, Leng L, Ding J, Zhao Y. Comput. Ind. Eng. 2023; 177: e109078.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.cie.2023.109078

PMID

unavailable

Abstract

To address the situation of insufficient reserve of resources in the initial stage of emergency rescue, a multi-objective location-allocation model for emergency supplies with timeliness and fairness being considered concurrently is proposed in this paper. The first objective is defined as time cost, including transport time cost and waiting time cost. The second objective is defined as the number of short supplies. The allocation of supplies is taken into account together with the urgency degree in a disaster area (DA), and the DAs with different urgency degrees are given different minimum allocation quantities of supplies to achieve fairness to a maximum extent. To solve this complex problem, a multi-objective hyper-heuristic (MOHH) optimization framework based on an evolutionary algorithm is proposed in this paper. In the framework, twelve low-level heuristics (LLHs) are designed with the actual information in the problem field being taken into account, and an online learning-based choice strategy is designed to choose high-quality and efficient LLHs. In addition, three acceptance criteria (AC) based on the D matrix are put forward to improve the performance of the MOHH framework. It is verified through comparative analysis experiments that the LLHs and the model are effective and the performance of the proposed multi-objective algorithm is better than that of NSGA-III and MOPSO.

Language: en

Keywords

Emergency logistics; Hyper-heuristic algorithm; Location-allocation problem; Multi-objective problem