Improved Particle Swarm Algorithm for Logistics Distribution Path Optimization

DOI

10.2991/978-94-6463-308-5_38How to use a DOI?

Keywords

logistics distribution problem; mathematical modeling; particle swarm algorithm; adaptive

Abstract

In this paper, the logistics distribution path problem is studied in depth, and the general steps of model establishment are analyzed and summarized through the study of logistics distribution models with many different objectives, and the logistics distribution model of multiple vehicles in multiple car parks based on the shortest path is established, while the number of customers served by the vehicles is restricted and new constraints are added from the perspective of controlling vehicle mileage. At the same time, multiple algorithms are analyzed and compared, and finally the particle swarm algorithm is chosen as the research object. By studying the shortcomings of the traditional particle swarm algorithm, an adaptive variation particle swarm optimization algorithm is designed. The article introduces fuzzy classification, adaptive variation mechanism, adding new variation probability and adjustable adaptation variance to achieve the purpose of adaptive adjustment of current particles, so as to avoid premature convergence and form a new adaptive variation particle swarm optimization algorithm. Finally, simulation experiments are conducted on the contents made through the platform to verify the corresponding conclusions. The simulation contents are to verify the feasibility and superiority of the optimization algorithm with the multi-vehicle model established in the paper, and to verify the different logistics distribution schemes obtained from the distribution models based on different target premises with the two models based on the shortest path least vehicle and based on customer satisfaction given in the previous paper. Two conclusions are obtained from the simulations, which are that the present algorithm has better features than the traditional particle swarm algorithm in solving such problems, maintaining a better global search capability and effectively avoiding premature convergence of the algorithm.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - XiangYu Zhang
AU  - TongJi Yang
PY  - 2023
DA  - 2023/12/11
TI  - Improved Particle Swarm Algorithm for Logistics Distribution Path Optimization
BT  - Proceedings of the 2023 8th International Conference on Engineering Management (ICEM 2023)
PB  - Atlantis Press
SP  - 353
EP  - 363
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-308-5_38
DO  - 10.2991/978-94-6463-308-5_38
ID  - Zhang2023
ER  -