Research on VRPTW of Optimizing Based on Hierarchy Clustering Method and HGA for Electronic Commerce

Abstract

Electronic commerce is a new commercial mode. Its logistics enterprise will face with such modern logistics distribution market of customers' individual and various demand, high service level and quality, high cost and risk. Therefore, the traditional vehicle scheduling is not easy to satisfy with the real demand of logistics distribution under electronic commerce so as that distribution cost cannot decrease, or it cannot satisfy with the customer's demand and lose the market competence for absent of flexible and timely characteristics. Therefore, according to the particularity of logistic distribution under electronic commerce environment, 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. And in every region it can be decomposed into small scale subsets according with some restraint conditions using scan algorithm. In the second phase, optimize the line of each single TSPTW model according to customers' dot in each group. Therefore, hybrid genetic algorithm is used to get the optimization solution, namely, deal with TSPTW model line optimization problem by dualistic coding so as to simplify the problem and improve the searching efficiency of genetic algorithm. Improved ordinal crossover operators can avoid destroying good gene parts during the course of ordinal crossover so as that the algorithm can be convergent to the optimization as whole. Adopt partial route overturn mutation operator 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 algorithm combining with examples.