Multi-Objective Cold Chain Logistics Optimization via Integrated K-means Clustering and Genetic Algorithm
Abstract
With the growing demand for fresh food, Cold Chain Logistics (CCL) faces challenges in meeting market demands. To reduce logistics costs and improve delivery efficiency, a cold chain distribution path planning model combining K-means algorithm and Genetic Algorithm (GA) has been proposed. The K-means algorithm is used for cluster analysis to generate initial candidate locations for distribution centers, while the GA determines optimal center positioning. By integrating the rapid convergence of K-means with the global search capability of GA, this approach resolves local optimization issues. Cluster effectiveness is evaluated using contour coefficients. Subsequently, a multi-objective optimization model incorporating real-time traffic conditions, product freshness preservation time, and vehicle load constraints is constructed. This model was validated using Google Open Routes Data (GORD) and Vehicle Routing Problem (VRPI) instance datasets. The results indicated that the fusion algorithm performed well in optimizing CCL distribution paths. The average task processing time of the fusion algorithm was controlled between 6.32 seconds and 8.42 seconds, with the lowest resource utilization rate of only 75.21% to 78.46%, and an average energy consumption value of 149.67 J to 160.72 J. The delivery cost and efficiency per kilometer were approximately 1.2 yuan and 40 km/h. The dynamic response capability of path planning has been significantly enhanced, effectively avoiding traffic congestion nodes and reducing cargo losses in cold chain transportation. This study has achieved collaborative optimization of distribution center location and path planning, which is of great significance for reducing operating costs, improving distribution efficiency, and promoting the construction of smart logistics systems.References
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https://doi.org/10.31449/inf.v49i36.9217Downloads
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