Research on vehicle route optimization for multi-center joint distribution at the end of e-commerce logistics integrating improved genetic algorithm
With the rapid development of e-commerce logistics,the issue of multi-center terminal distribution has re-ceived widespread attention.However,current vehicle distribution problems generally focus on path optimization for a single logistics center,and do not take into account multi-center distribution situations where resource scheduling is more complex.The research uses mathematical modeling of the path problem,and uses the genetic algorithm of the double-layer chromosome coding mode to optimize the multi-center distribution vehicle path at the end of e-com-merce logistics.During the research process,it was found that traditional genetic algorithms tend to fall into local opti-mal solutions and appear premature.In order to solve this problem,adaptive adjustment of crossover probability and mu-tation probability is studied to improve the algorithm.Thus,a distribution vehicle route optimization model integrating genetic algorithm was constructed.Through experimental analysis,it can be seen that the model reduces the path length by more than 11％compared with other model optimization results,and can achieve high-quality path optimization and save costs and time for logistics distribution.
万方数据
维普
