Study On Joint Distribution Vehicle Routing Problem Based On Urban Logistics

City logistics is closely related to the development of the city and people's life.With the increase of demand,the amount of logistics is rising,the number of vehicles into the city is also increasing.This not only leads to traffic congestion,but also causes major pollution of urban air.In order to ease traffic pressure and reduce urban pollution,traffic restrictions policies have been implemented in major cities,like Beijing,Shanghai,Guangzhou and other cities.Traffic restrictions made urban distribution networks separated,no longer coherent,which brought many problems to the city logistics and distribution.Traffic restrictions also made the vehicle into the city in period time,and vehicle subject to many restrictions.The demand of urban distribution is not subject to time and geographical constraints.Therefore,we proposed a cross-region joint distribution strategy.Firstly,we analyzed the basic structure of city logistics distribution.In order to deal with the urban distribution problem under traffic restrictions policies.We also proposed a cross-region distribution strategy,and analyzed the impact of urban logistics on the environment.We considered environment as an impact factor of objective function.Secondly,we divided customer service area by customers' time window or customers' geographic position,then provided distribution services at different period and stage.Then we proposed networked multi-stage cross-region distribution problem,which was based on the traditional multi-depot vehicle routing problem.In that case,we established a multi-stage cross-region joint distribution model.Which considered the distribution distance,vehicle rate,driving speed and other factors,and designed the cost of vehicle fuel and fixed costs of start-up as the optimization goal.Finally,for the features of the model,we combined cluster method and scan method to generate the initial population,designed an adaptive crossover and mutation probability,improved main genetic operator.We also designed a more adaptive genetic algorithm to adapt to this paper.Using MATLAB programming language programmed algorithm.Then we used numerical examples to verify the algorithm.In this paper,we proposed an improved model which effectively save mileages,reduce distribution paths and costs,designed algorithms has also achieved well results.