Research On The Vehicle Routing Problem With Simultaneous Pickup And Delivery And Uncertain Information

In the distribution activities, the vehicle routing problem of distribution is one of the key problems to realize distribution rationalization, selecting the reasonable vehicle routing distribution program can help reduce the enterprise cost, improve the service quality, increase economic efficiency and enhance the customer satisfaction on the part of logistics.In order to reduce distribution cost that is to avoid the no-load phenomenon that the traditional vehicle routing problem with pure pickup or delivery brought about, and improve customer service level, the vehicle routing problem with simultaneous pickup and delivery and uncertain information was researched from three aspects including simultaneous pickup and delivery, customer fuzzy time windows and time-dependent travel time in this paper.This paper mainly analyzed and researched the problem from the following aspects.Firstly, the concept of vehicle routing problem was described, the original problem, characteristic and classification of vehicle routing problem were analyzed and summarized and the vehicle routing problem solving algorithm was reviewed and summarized.Secondly, the fuzzy membership function that expresses customer satisfaction and the road time-dependent travel time were introduced, and the model of the vehicle routing problem with simultaneous pickup and delivery and uncertain information was established, which was taking to minimize the vehicle total distance, minimize the vehicle number that be used, minimize the vehicle average waiting time and maximize the average customer satisfaction as the goals. As the vehicle routing problem is NP-Hard problem, according to the characteristic of the new model, combining the advantage of genetic algorithm and tabu search algorithm, a genetic and tabu search algorithm for solving the problem was designed in this paper. At last, the genetic and tabu search algorithm of the new model was implemented under Matlab IDE, and simulation test was made by the example. Simulation results showed that the new model had good effect, the genetic and tabu search algorithm was reliable and effective. The comparative analysis showed that the solution quality was upgraded and better than the results of comparison algorithm and generally.