Study on location-allocation problem and algorithm for emergency supplies considering timeliness and fairness

•A bi-objective formulation for emergency supply chains considering timeliness and fairness.•Multi-objective evolutionary framework considering hyperheuritic approach, mutation heuristics, and local search heuristics.•An online learning-based choice function and three acceptance criteria with matrix...

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Veröffentlicht in:Computers & industrial engineering Jg. 177; S. 109078
Hauptverfasser: Wang, Zheng, Leng, Longlong, Ding, Junjie, Zhao, Yanwei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.03.2023
Schlagworte:
Emergency logistics
Hyper-heuristic algorithm
Location-allocation problem
Multi-objective problem
Emergency logistics
Hyper-heuristic algorithm
Multi-objective problem
Location-allocation problem
ISSN:0360-8352
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Zusammenfassung:•A bi-objective formulation for emergency supply chains considering timeliness and fairness.•Multi-objective evolutionary framework considering hyperheuritic approach, mutation heuristics, and local search heuristics.•An online learning-based choice function and three acceptance criteria with matrix D as high-level strategy. 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.
ISSN:0360-8352
DOI:10.1016/j.cie.2023.109078