Research Of Vehicle Routing Problem In Supply Chain

In recent years, with the rapid development of national economy, the position of logistics industry is more and more prominent with large scale extension. At the same time, the operation efficiency and concentration of logistics industry need to be enhanced more, but high operation cost is a bottleneck problem for logistics industry.As the main part of logistics activities, distribution becomes the research focus.Among them, path planning of transportation is the main aspect, whether vehicle path is reasonable will direct effect the speed, cost and benefit of distribution. In recent years, researchers have proposed lots of algorithm in order to solve path planning problem during distribution. Among them, the genetic algorithm is an effective approach.Taking into account the traditional genetic algorithm in solving the problem of vehicle routing planning, there is a long time in the process of solving the vehicle routing problem. The algorithm is complicated, and the precision is difficult to improve. The algorithm is trapped in local optimum. In particular, the algorithm is premature convergence and the local optimal problem. Therefore, this article, from the problem of premature convergence of the algorithm, to seek the improvement measures of genetic algorithm, and to solve the problem of vehicle routing problem with time windows, which is more general than the common vehicle routing problem.Through this article, the basic theory of vehicle routing problem, such as definition,composition and classification, is introduced, and the model of vehicle routing problem with time window is established, and the model parameters are defined in detail. The theory of several common algorithms and advantages and disadvantages for vehicle routing problems are introduced.Secondly, the basic ideas, basic concepts and basic steps of the traditional genetic algorithm are introduced in detail. The principle of selection operator, crossover operator and mutation operator is introduced in the article, which lays the foundation for the next chapter.Finally, for the vehicle routing problem with time window, a series of improvement measures are proposed for the traditional adaptive genetic algorithm. By considering population correlation, the concentration of the individual and individual fitness adaptive selection, crossover and mutation operators, not only to enhance the convergence speed, and improves the colony diversity, enhance the ability of global search. By comparing the simulation results of the improved adaptive genetic algorithm and the traditional adaptive genetic algorithm to the vehicle routing problem with time windows, the superiority of the improved genetic algorithm in solving the distribution route optimization problem is verified.