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Aiming to large-scale Multiple-Depot Food transport Vehicle Routing Problem (MDFVRP), this study proposed an improved genetic algorithm solution frame based on the two-stage fuzzy clustering. In the static upper stage, the k-means technology is used to divide the MDFVRP into several one-to-many sub-problems. From the perspective of improving the customer satisfaction and integrating logistics resource, the lower fuzzy clustering stage adopts fuzzy clustering algorithm to form the dynamic customer base based on customer’s order distribution according to customers request attributes. Furthermore, the Genetic Algorithm (GA) of VRP is designed through the improvement of the selecting operator and the crossover operator. The stochastic simulation experiments show the proposed algorithm and solution strategy are efficient.

Multiple-depot Food Transport Vehicle Routing Genetic Algorithm Based on Two-stage Fuzzy Clustering