Research on VRPTW of Optimizing Based on Fuzzy c-means Clustering and IGA under Electronic Commerce

The logistic distribution under electronic commerce has the characteristic of dispersive customer positions, large order forms, little batches and many repeated routes. The traditional optimizing vehicle routing problems with time windows meet with diversified problems in different extents and are difficult to play their roles. Therefore, the improved two-phase algorithm needs to be adopted to get solutions. Namely, the customer group can be divided into several regions using fuzzy c-means clustering algorithm in first phase. And it is decomposed into some small-scale subsets according with restraint conditions with scan algorithm in each region. In second phase, it is route optimization problems of several single TSPTW model. Therefore, the study proposes the improved genetic algorithm, which using dualistic coding so as to simplify the problem and to improve the searching efficiency of genetic algorithm, using individual amount control selection game in order to guarantee group diversity, using partially matched crossover and partial route overturn mutation that combined hill-climbing algorithm to improve convergent speed of algorithm so as to better solve the inconsistency between diversity and convergent speed. In the end, the test proves the validity of this improved cluster first/route second algorithm combining with examples.