An Improved Adaptive Genetic Algorithm for the Multi-depot Vehicle Routing Problem with Time Window

Abstract

In order to improve the efficiency of vehicle objective, the paper addresses the problem of multi-depot vehicle routing with time window. An adaptive genetic algorithm based on the artificial bee colony algorithm is developed for the solution process of the multi-depot vehicle routing problem. The new algorithm provides not only with the strong global search capability, but also the strong local search capability. Give the multiple depots vehicle scheduling model and the coding method of the vehicle route. On the one hand, in order to increase the accuracy of optimization and reduce the probability of trapping in local optimum, adjust adaptively the ratio of the crossover and mutation. On the other hand, the acceptance operators are treated by the simulated annealing. The fitness function with the adaptive penalty coefficient is designed. The simulation results demonstrate that the solving result of the fusion algorithm is more excellent than the other algorithms, and it improves the performance in searching speed and increases the global astringency compared with simple genetic algorithm.