Application of Improved Binary K-means Algorithm in Time and Cost Optimization for Regional Logistics Distribution Center Location - Wang, Dandan (Informatica, 2025)
The surge in express delivery volume has heightened the importance of addressing customer distribution needs. As a critical component of the logistics supply chain, the regional logistics distribution center requires strategic site selection to enhance service quality and reduce operational costs. This study proposes an optimized location model for regional logistics distribution centers based on an improved binary K-means clustering algorithm, focusing on minimizing distribution time and enterprise costs. In the initial stage, Z-score normalization was applied to preprocess the data and eliminate dimensional effects. The initial cluster centers were selected randomly from demand points to mitigate the risk of local optima. The model employs the time spent on the journey as the primary objective function while also incorporating enterprise investment cost and operational risk cost. With an initial setting of 9 cluster centers and a maximum of 100 iterations, the model demonstrated rapid convergence. Experimental results indicate that the total distribution time was reduced to 18,800 minutes, representing a 33.6% decrease compared to the conventional K-means model. The optimized solution identified 6 distribution centers, with the average distance from each center to demand points maintained below 1 km, effectively lowering enterprise costs and risks. The findings highlight the efficiency of the improved binary K-means clustering algorithm in optimizing time and cost, providing valuable insights for strategic site selection of regional logistics distribution centers.