Author(s)

Kan Liu ¹, Hailan Pan ¹, Yanjiang Li ¹

Affiliation(s)

¹ School of Economics and Management, Shanghai Polytechnic University, Shanghai, 201209, China

Corresponding Author

Hailan Pan

ABSTRACT

In recent years, the logistics industry continues to grow, distribution center as a key node in the logistics system, in reducing logistics and distribution costs, improving the efficiency of service response has a very important strategic value. Therefore, the reasonable planning of logistics warehouse site selection is to establish a mathematical model that considers the minimum total cost and the minimum carbon emission, and use an improved particle swarm algorithm to make the decision. This algorithm incorporates genetic algorithms based on traditional particle swarm algorithms, and adds chaotic mappings to the solution process to obtain a more uniform initial distribution and random perturbations, thereby enhancing global search capability. The experimental results show that the improved particle swarm algorithm provides a basis for helping enterprises to reduce logistics costs, carbon emissions in various links and to improve the efficiency of logistics management. The experimental results show that the improved particle swarm algorithm provides a basis for helping enterprises to reduce logistics costs, carbon emissions in various links and environmental issues management by selecting the optimal warehouse nodes.

KEYWORDS

Warehouse Siting; Improved Particle Swarm Algorithm; Total Cost Minimization; Carbon Emission Minimization; Genetic Algorithm

CITE THIS PAPER

Kan Liu, Hailan Pan, Yanjiang Li, Research on Logistics Warehouse Site Selection Problem Based on Improved Particle Swarm Optimization Algorithm. Information Systems and Economics (2025) Vol. 6: 97-106. DOI: http://dx.doi.org/10.23977/infse.2025.060213.

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