The improved Memetic algorithm for two-echelon on vehicle routing optimization

According to the problems of the low accuracy and long convergence time of traditional method in solving the two-echelon vehicle routing problem, a kind of Memetic algorithm with Q learning theory and differential evolutionary was proposed. Firstly, the two-echelon vehicle routing problem was studied, and the optimum partition method was used to obtain the reasonable distribution plan for SDVRP problem in first stage, based on which the total mileage and total delivery vehicles were determined for both the two stages. Secondly, according to the second level of MDVRP distribution scheme, the Memetic algorithm was designed with Q learning theory and differential evolution algorithm, which was used to achieve the global optimization of MDVRP distribution scheme. Finally, through simulation experiments verify the effectiveness of the proposed algorithm.