A Functional Area Layout Model of Agricultural Products Logistics Park Based on PSO Algorithm

Wanning Zhao, Fuyang Zhao
Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing, PMLR 278:789-797, 2025.

Abstract

This paper proposes a functional area layout model for an agricultural products logistics park based on particle swarm optimization ( PSO ). Targeting the the layout planning of an agricultural products logistics park in C County, a multi-objective planning model is established ,considering the total material handling cost, land area utilization rate, and the comprehensive correlation of functional areas. The PSO algorithm is employed to solve the model and obtain the optimal layout scheme. Through field research and data analysis, the validity and practicability of the model are verified. The results indicate that the model can significantly enhance the operational efficiency and service quality of agricultural products logistics parks, reduce logistics costs, and promote the sustainable development of the agricultural products logistics industry.

Cite this Paper

BibTeX
@InProceedings{pmlr-v278-zhao25d, title = {A Functional Area Layout Model of Agricultural Products Logistics Park Based on PSO Algorithm}, author = {Zhao, Wanning and Zhao, Fuyang}, booktitle = {Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing}, pages = {789--797}, year = {2025}, editor = {Zeng, Nianyin and Pachori, Ram Bilas and Wang, Dongshu}, volume = {278}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v278/main/assets/zhao25d/zhao25d.pdf}, url = {https://proceedings.mlr.press/v278/zhao25d.html}, abstract = {This paper proposes a functional area layout model for an agricultural products logistics park based on particle swarm optimization ( PSO ). Targeting the the layout planning of an agricultural products logistics park in C County, a multi-objective planning model is established ,considering the total material handling cost, land area utilization rate, and the comprehensive correlation of functional areas. The PSO algorithm is employed to solve the model and obtain the optimal layout scheme. Through field research and data analysis, the validity and practicability of the model are verified. The results indicate that the model can significantly enhance the operational efficiency and service quality of agricultural products logistics parks, reduce logistics costs, and promote the sustainable development of the agricultural products logistics industry.} }
Endnote
%0 Conference Paper %T A Functional Area Layout Model of Agricultural Products Logistics Park Based on PSO Algorithm %A Wanning Zhao %A Fuyang Zhao %B Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing %C Proceedings of Machine Learning Research %D 2025 %E Nianyin Zeng %E Ram Bilas Pachori %E Dongshu Wang %F pmlr-v278-zhao25d %I PMLR %P 789--797 %U https://proceedings.mlr.press/v278/zhao25d.html %V 278 %X This paper proposes a functional area layout model for an agricultural products logistics park based on particle swarm optimization ( PSO ). Targeting the the layout planning of an agricultural products logistics park in C County, a multi-objective planning model is established ,considering the total material handling cost, land area utilization rate, and the comprehensive correlation of functional areas. The PSO algorithm is employed to solve the model and obtain the optimal layout scheme. Through field research and data analysis, the validity and practicability of the model are verified. The results indicate that the model can significantly enhance the operational efficiency and service quality of agricultural products logistics parks, reduce logistics costs, and promote the sustainable development of the agricultural products logistics industry.
APA
Zhao, W. & Zhao, F.. (2025). A Functional Area Layout Model of Agricultural Products Logistics Park Based on PSO Algorithm. Proceedings of 2025 2nd International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 278:789-797 Available from https://proceedings.mlr.press/v278/zhao25d.html.

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