Affiliations: [a] School of Law, Humanities and Sociology, Wuhan University of Technology, Wuhan, Hubei, China | [b] Research Center on Urban and Rural Public Governance in China, Wuhan, Hubei, China | [c] School of Economics, Wuhan University of Technology, Wuhan, Hubei, China
Correspondence:
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Corresponding author: Zhixiang Yin, School of Law, Humanities and Sociology, Wuhan University of Technology, Wuhan, Hubei 430070, China. E-mail: [email protected].
Abstract: Nowadays, the demand for risk response is increasing in countries worldwide, leading to the development of emergency-related industries as strategic emerging sectors. However, the emergency logistics industry is facing increasingly critical distribution issues. This study applies K-means clustering analysis to convert multiple distribution centers into multiple single distribution center problems. It then compares and analyzes the vehicle routing model with time windows for emergency logistics delivery in multiple distribution centers using guided local search (GLS), taboo search (TS), and simulated annealing (SA) algorithm. The results demonstrate that the GLS algorithm outperformed both the SA and TS algorithm in optimizing emergency logistics delivery paths for multiple distribution centers. The GLS algorithm proved to be more effective in solving this problem. This study confirms the contemporary value of emergency logistics distribution problems and offers practical insights into optimizing emergency logistics distribution paths in multiple distribution centers.
Keywords: Emergency logistics, distribution path, K-means, guided local search algorithm, multiple distribution centers
