| Issue |
Int. J. Simul. Multidisci. Des. Optim.
Volume 16, 2025
Innovative Multiscale Optimization and AI-Enhanced Simulation for Advanced Engineering Design and Manufacturing
|
|
|---|---|---|
| Article Number | 22 | |
| Number of page(s) | 14 | |
| DOI | https://doi.org/10.1051/smdo/2025023 | |
| Published online | 07 October 2025 | |
Research article
Warehouse location optimization in fresh cold chain logistics based on PSO-GA
School of Management, Henan Institute of Economics and Trade, Zhengzhou, 450000, PR China
*e-mail: lina_guogg@outlook.com
Received:
29
June
2025
Accepted:
31
August
2025
This study focuses on the site selection problem of fresh cold chain logistics warehouses, using a site selection model to minimize operating costs, improve distribution efficiency, and meet customer needs. The model covers warehouse location selection, special requirements for the fresh and cold chain, and organization of delivery routes. At the same time, a solution algorithm combining genetic algorithm and particle swarm optimization algorithm is proposed to solve the problem of location selection model. This hybrid algorithm encodes the layout problem of logistics points into a chromosome problem in genetic algorithms and utilizes particle swarm optimization to improve the efficiency of the search process and avoid early convergence difficulties. The results indicated that the improved algorithm was more effective than the ordinary genetic algorithm. After only 100 iterations, the objective function value of the algorithm decreased to approximately 31,500. The average total delivery cost of the model was reduced to 75.8369 million yuan, and the calculation was completed within 75 s, with significant efficiency. Therefore, this model can effectively assist logistics enterprises in accurately formulating economically effective distribution plans, achieving the optimal balance between cost and efficiency.
Key words: PSO / GA / Cold chain logistics / Warehouse location
© L. Guo and Y. Wei, Published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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