Location optimization of logistics distribution centers based on improved salp swarm algorithm

Guangqiang Li; Ruoxi Liu; Lehao Xie; Hao Chen; Xinyue Chen; Xunkai Zhu

doi:10.1117/12.3049688

8 November 2024 Location optimization of logistics distribution centers based on improved salp swarm algorithm

Guangqiang Li, Ruoxi Liu, Lehao Xie, Hao Chen, Xinyue Chen, Xunkai Zhu

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134160N (2024) https://doi.org/10.1117/12.3049688
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

Focusing on the multi-objective optimization models, in order to solve the location problem of logistics distribution centers more satisfactorily, we propose an improved multi-objective salp swarm algorithm (IMSSA). Based on the original salp swarm algorithm (SSA), the following improvement measures are put forward. Firstly, in order to be suitable for solving multi-objective problems, we introduce the concepts of non-dominated sorting, crowding distance and external archives to update basic SSA into multi-objective SSA. Then, a leader position update strategy is adopted based on the idea of evolutionary game theory. In addition, simulated annealing mechanism is applied to improve the position update strategy of followers. Finally, the proposed IMSSA is employed to optimize the location of distribution centers of a bidirectional two-tier logistics network, and the comparative analysis of the Pareto solution sets verifies the feasibility and effectiveness of the proposed algorithm.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guangqiang Li, Ruoxi Liu, Lehao Xie, Hao Chen, Xinyue Chen, and Xunkai Zhu "Location optimization of logistics distribution centers based on improved salp swarm algorithm", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134160N (8 November 2024); https://doi.org/10.1117/12.3049688

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