Research on multi-depot VRPTW of optimizing based on hierarchy clustering method and HGA for electronic commerce

The traditional vehicle scheduling is not easy to satisfy with the real demand of logistics distribution under electronic commerce. Therefore, according to the particularity of logistic distribution under electronic commerce, multi-depot vehicle routing problem with time windows model is built. For MDVRPTW is NP puzzle, the improved Two-Phase Algorithm needs to be adopted to get solutions. Namely, the customer group can be divided into several regions using hierarchy clustering method in first phase. In the second phase, optimize the line of each single VRPTW model according to customers´ dot in each group. Therefore, hybrid genetic algorithm is used to get the optimization solution. Use dualistic coding to deal with the time constrain of VRPTW problem, which can makes the problem simpler. Use improved saving algorithm to construct initial solution to improve the genetic low´s searching efficiency. Using the best retain select method to ensure the diversity of groups. Improved ordinal crossover operators can avoid destroying good gene parts during the course of ordinal crossover. Adopt partial route overturn mutation operator to improve convergent speed. In the end, the test proves the validity of this improved algorithm combining with examples.