Two-echelon vehicle path optimization based on Memetic algorithm
Article
Figures
Metrics
Preview PDF
Reference
Related
Cited by
Materials
Abstract:
Aiming at the problem of low accuracy and long convergence time of traditional method in solving the two-echelon vehicle routing problem, we proposed a kind of Memetic algorithm based on Q learning theory and differential evolution. Firstly, the two-echelon vehicle routing problem was studied, and the optimum partition method was used to obtain the reasonable distribution plan for SDVRP(split delivery vehidle fouting problem) in first stage, and then the total mileage and delivery vehicles were determined for both the two stages. Secondly, according to the distribution scheme of the second level of MDVRP(multi-depot vehivle fouting problem), 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 verified the effectiveness of the proposed algorithm.
