The Research Of Vehicle Routing Problems With Time Window Based On Electric Vehicle

Nowadays, China’s large and medium-sized cities are facing air pollution problems. China’s urbanization process on the one hand caused car ownership of urban residents in China increased rapidly, on the other hand, leading to the city logistics distribution soared. According to the survey,60%～70% CO and 40% NOX in the air is from automobile exhaust. Therefore, energy saving and emission reduction of the car will be the key role to solve the problem of air pollution. If the electric vehicle, which is pollution-free, high efficiency, can be widely promoted, will undoubtedly benefit to solve the problem of pollution in the city. With the improvement of national laws and regulations and the publication of supporting policies on electric vehicles, electric cars are entering into thousands of households. In addition, the logistics enterprises of our country are in the test of electric vehicles used in the field of logistics distribution. In February 2013,70 electric vehicles are used in logistics distribution in Beijng. In July 2013, the first electric vehicle whose charge mode is changing the battery in our country is used in Zhengzhou. Compared to conventional vehicles, pure electric vehicles is short of driving range, long of charging time, lack of charging station. The model of optimal routing problem in the traditional vehicle is not adapts the electric vehicles, but the relevant research is deficiency in our country. Therefore, this thesis researches the optimal route problem for electric vehicles.This thesis mainly does the following research:First, the thesis reviews the relevant previous research about optimal routing problem and electric vehicle operation management. Then, the mathematical model with time penalty to optimizing VRPTW which was added the characters of the electric vehicle was established.Second, this paper analyses a variety of optimization algorithms of resolving vehicle routing problem with time windows, and finally adopts genetic algorithm as the solving algorithm of the model. This paper designs the chromosome encoding and special arithmetic operators which are fit for the model. Ultimately confirms the feasibility of this model and algorithm by an example.