Abstract: Information enables the iterative upgrading of traditional logistics industry. In order to solve the transportation problems caused by the unique logistics characteristics of automobile manufacturing industry, it considers the improvement of the speed for milk-run, reducing the cost and alleviation the traffic pressure caused by logistics vehicles in the city, based on the actual transportation demands of milk-run of automotive equipment manufacturer A in city Q. An intelligent path planning method for automobile parts transportation based on improved GA(Genetic Algorithm) algorithm is designed. The genetic algorithm is improved by using the coupling factors such as the demand of parts and components in the current month, the details of supplier orders, the capacity rate of optional transportation vehicles, the volume proportion of single vehicle appliances, and the demand of time window in the process of milk-run. In this way, the optimal path using Solomon data example is solved and compared with genetic algorithm, and the optimal distribution scheme for solving the actual transportation demands between A and the suppliers. The experimental results show that the method has some advantages in performance. The numerical simulation results illustrate the applicability of the method and the convergence in the optimization process.

Key words: milk-run,  , time window requirement,  , vehicle capacity rate,  , optimal distribution scheme