Optimal Number and Sites of Regional Logistics Centers by Genetic Algorithm and Fuzzy C-mean Clustering

Optimal number, sites, and scale of regional logistics centers were studied by using fuzzy c-means clustering and genetic algorithms. The uncertainty for the clients to select logistics centers and exchanging amount was defined as the membership of the clients to the given logistics centers or the clustering centers. The synthetic evaluation indices of features vectors were established. Then we got the model of logistics centers scale and the model can be transferred into a nonlinear programming problem. The rules function of logistics centers selecting was defined. The number of logistics centers or the optimal c value was determined by selecting the minimal rules function. Genetic algorithm was adopted in the concrete course for solution. The theory and the method were applied in the logistics system arrangement and programming of Langfang City in China. The concrete simulation result shows its correctness and feasible.