Improved Fuzzy Clustering for Logistics Site Selection and Optimization of 2E Vehicle Routes

In response to problems such as high costs and inconveniences faced by farmers in remote areas during the process of goods collection, this study proposes a two-echelon collaborative location-routing optimization transportation mode, aiming to achieve full utilization of resources and reduction of transportation costs. To solve this problem, a mathematical model is constructed with the objectives of minimizing the total cost of enterprises and maximizing the satisfaction of transfer stations. To solve this model, an algorithm integrating reachable distance-based genetic simulated annealing fuzzy C-means clustering with LOCAL SEARCH-NSGA-II is designed, hereinafter referred to as the hybrid FCM-LS-NSGA-II algorithm. The solution process consists of three steps: first, using the clustering strategy of the reachable distance-based genetic simulated annealing fuzzy C-means clustering algorithm to classify customers and initially construct distribution routes; second, optimizing the distribution routes through the local search (LOCAL SEARCH) strategy to improve the maximum satisfaction of transfer stations; finally, solving the calculation examples and optimization cases. The results show that the hybrid FCM-LS-NSGA-II algorithm has good optimization performance and can provide effective solutions for urban managers and logistics enterprises.