Research And Design Of Vehicle And Cargo Dynamic Loading Model Based On Improved Genetic Algorithm

Highway logistics occupies the leading position of freight transport in China.In recent years,with the rapid development of economy in our country,the traffic business of highway logistics has been increasing year by year,which has brought unprecedented opportunities for development of China logistics industry.A large number of logistics information service platforms have emerged.However,with the increase of logistics business,there are still many problems in the current logistics loading,such as the lack of perfect logistics information standardization,the low intelligence level of information service platform,the lower loading efficiency and the imperfect construction of information service platform credit system.Therefore,it is of great significance to explore the loading mode and system of vehicles and cargo to provide reference for our country’s logistics industry,and to realize the optimal allocation of social resources.Based on the research of relevant literature at home and abroad and fully enterprise investigation,this paper firstly studies the current situation of vehicle and cargo loading service platform,classifies platforms,analyzes the functions and requirements of the platform.At the same time,the existing problems are found,the business process of vehicle and goods dynamic loading and dispatching system are designed.Then,a preliminary matching method based on attributes is designed by the analytic hierarchy process and confused evaluation method,and an example is given to verify it.On the basis of attribute matching,the dynamic loading model of vehicle and goods is designed,which takes the truck driver’s income as the model objective function with the maximum load,the operation time window and the path rati onality as constrains.Then an improved genetic algorithm is used to get the model solution.The algorithm firstly combines the ant colony algorithm to initialize the chromosome population,and then it is adapted to the problem model in the process of crossover,mutation,selection,and population adjustment.Finally,the improved genetic algorithm presented in th is paper is simulated.The experimental process is analyzed in detail about the performance of the improved genetic algorithm in efficiency and accuracy.The results verifies the feasibility of the algorithm in the dynamic loading process.