Research And Simulation On Vehicle Routing Problem Based On Information Platform

Vehicle Routing Problem is the focusing research of the Intelligent Transportation System (ITS). Developing and constructing Vehicle Optimizing Routing System is a sort of scientific approach of lowering transportation cost and promoting the development of the ITS. The research on the model and the optimization algorithms of Vehicle Routing Problem also has important significance and practical value to enhance the intelligence of Transportation System.This thesis makes research on Vehicle Routing Problem with the known assignments and Vehicle Routing Problem with the known routes. The main work is as follows:Firstly, on the background of logistics an improved model of Vehicle Routing Problem with the known assignments is introduced. The model takes into account the traffic status and the time requested by clients. The impedance function is used to reflect the actual traffic status.Secondly, there is a little research on the Particle Swarm Algorithm, but it is easier to realize than the other intelligent algorithms. The Simulated Annealing Algorithm has advantages in solving combination optimization. So a Hybrid Particle Swarm Algorithm, which is combined the Particle Swarm Algorithm and the Simulated Annealing Algorithm, is proposed to solve the Vehicle Routing Problem with the known assignments. The model and the Algorithm are calculated and simulated through programming. The result of Simulation shows the validity of this method.Thirdly, on the background of public transportation an improved model of Vehicle Routing Problem with the known routes is introduced. The model takes into account the benefit of the public transportation company and the benefit of passengers. The weighting method is used to translate the multi-objective optimization into the single- objective optimization.Fourthly,the scheme based on Genetic Algorithm is designed aim at the model of Vehicle Routing Problem with the known routes. Its fitness function is the objective function transformed. Selection Operator is Proportional Model. Crossover Operator is Two-point Crossover. Mutation Operator is Inverse Mutation. Elitist Model is used in the GA. Program simulates the model and the GA. The optimization result is analyzed.Lastly, the design idea and the realization scheme of Vehicle Optimizing Routing System is given. Based on the real traffic investigation data of Xi'an City, the models and the optimization algorithms researched in the front chapters are applied to the routing of the logistic delivery and the scheduling of 29 route public transportation of Xi'an city. The optimization result is analyzed and the scheduling scheme is given.