Cold Chain Logistics Transportation Route Optimization Methods Based on Particle Swarm Self-evolution

HUANG Jilei

doi:10.19554/j.cnki.1001-3563.2025.11.031

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Packaging Engineering ›› 2025, Vol. 46 ›› Issue (11) : 285-293. DOI: 10.19554/j.cnki.1001-3563.2025.11.031

Green Packaging and Circular Economy

Cold Chain Logistics Transportation Route Optimization Methods Based on Particle Swarm Self-evolution

HUANG Jilei

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Abstract

The work aims to propose a cold chain logistics transportation route optimization method based on particle swarm self-evolution, so as to optimize the cold chain logistics transportation route and improve overall efficiency. Under constraints such as time and load, an objective function that integrated penalty functions was constructed to minimize total costs and maximize customer satisfaction, thereby improving service quality. A multi-layer and multi-branch tree composed of logistics centers and supply warehouses was constructed to more meticulously depict the structure and characteristics of the cold chain logistics network. The initial positions and velocities of individual particles were calculated to obtain route parameters. The particle swarm optimization algorithm was improved using crossover and mutation operators. By determining and updating the state, the self-evolution correction velocity of the particle swarm was calculated to solve the cold chain logistics transportation route optimization function, thereby improving the efficiency of cold chain logistics transportation route optimization. Experimental results demonstrated that the proposed method could efficiently complete delivery tasks, with a punctuality rate consistently maintained at over 98%. In conclusion, the proposed method optimizes the transportation route effectively while ensuring service quality and maximizing resource utilization.

Key words

particle swarm self-evolution / cold chain logistics / mutation operator / optimization of transportation routes / crossover operator

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HUANG Jilei. Cold Chain Logistics Transportation Route Optimization Methods Based on Particle Swarm Self-evolution[J]. Packaging Engineering. 2025, 46(11): 285-293 https://doi.org/10.19554/j.cnki.1001-3563.2025.11.031

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