Study Of Multiple-depot Dynamic Vehicle Routing Problem Based On The Traffic Flow

In the part of logistics activities, the transportation and distribution plays a key role, and the distribution cost accounts for 40%-50% in the entire logistics activity cost. The rapid development of the modern logistics has put forward new requirements to the each link of logistics performance, how to effectively reduce operating costs and improve logistics efficiency is the problem to be solved. With the expanding of enterprise scale and customers’ demand, single distribution center has been difficult to meet demand, More and more companies began to establish multiple depots to complete the tasks, in order to improve the service efficiency and customers’satisfaction. And with the increase of vehicle number, the traffic congestion situation can be seen everywhere, traffic congestion not only brings a lot of traffic pressure, and from the angle of logistics operation, it greatly reduces the transportation efficiency, additional logistics cost has increased a lot. Obviously it conflicts with the direction of the development of modern logistics, so this article does the research about the multiple-depot dynamic vehicle routing problem based on the traffic flow.This article first expounds the meaning and significance of this study from the research background and research status, Through the analysis of traffic flow and MDVRP theory knowledge, the influence of the traffic flow for MDVRP has been clear, and combining previous research, this article established the multiple-depot vehicle routing palnning model under the condition of traffic flow for normal distribution. When solving the model, the article use method of decomposition and the overall method, then combined with the genetic algorithm, and do the simulation calculation with MATLAB. Combined with the conditions of different time distribution of traffic flow for each hour interval, vehicles starting in different time are calculated respectively, including distribution costs and the specific path planning. The results showed that the decomposition method has certain limitation, and has weakened the influence of traffic flow, the optimization effect is not obvious. Through the comparison, it found that overall method has better results, and more conducively to find the optimal solution in the global scope. Finally it chooses the cost of the smallest start time as the best start point in time, the corresponding path as the optimal moving path.