The Research Of Multiple Depot Vehicle Routing Problem With Simultaneous Pick-up And Deliveries

Distribution is an important element in modern logistics system. It includes picking up goods from distribution center and delivering goods to the customers on time. Among distribution business there are many optimizing strategies. The vehicle routing problem has great effect on improving distribution speed, quality of service and economy benefit. According to the number of distribution center, the vehicle scheduling problem can be divided into single-depot vehicle routing problem and multi-depot vehicle routing problem. The modern city logistics system usually has more than one depot. So this paper has both theoretical and practical value.For MDVRPSPD, we mainly research on those aspects in the following paper:Firstly, we study the traditional theory of the vehicle routing problem. Based on the research and the real needs, we proposed a multi depot vehicle routing problem with pick-up and delivery and established a mathematical model about the problem.What is more, we compared and analyzed the method of solving the vehicle routing problem as well as pointed out the shortcoming of the traditional method of solving the problem. For increasing the virtual distribution center, we proposed a method to translate vehicle routing problem with multi distribution centers into vehicle routing problem with single distribution center, and then using the traditional method to solve the vehicle routing problem, which only has a single distribution center.At last, for the shortcoming of genetic algorithm, which is easy to fall into local optimal solution, we proposed the use of cloud droplets in the cloud model that has the randomness and stable tendency to adjust the crossover and mutation probability. This performance improved the speed and feasibility of genetic algorithm. Finally, the improved genetic algorithm is also used to solving the MDVRPSPD. And we also compared the results of its findings and the result solving by standard genetic algorithm as well as the result of the traditional method. By comparing, this paper reflected the feasibility and effectiveness of the improved algorithm.