| In recent years, Logistics Distribution has become an important "third-party source of profit ", an important part in national economy. Vehicle Routing Problem the is core content area of Logistics Distribution. Therefore, the research on the vehicle routing problem is very important theoretical and practical significance. Today, many logistics companies usually have more than one distribution center (depot). Multiple-Depot Vehicle Routing Problem has become an important new area of research.MDVRP is NP hard problem. Its optimal solution can not be obtained in polynomial time. Modern heuristic settling for less obtaining suboptimal solution of the problem within an acceptable time, has become an important research direction. This article uses an improved genetic algorithm to solve the problem. Genetic algorithms is bioniced by simulate biological evolution of natural selection and genetic mechanism of biological evolution.Using Chromosome crossover and mutation process in the evolutionary process to global search better solution within the solution space. In this paper, I have improverd genetic algorithm,with natural number coding chromosome, improved crossover operator and increasing the external perturbation techniques within the study multi-depot vehicle routing problem.The main work of this paper. First through the multiple depot vehicle routing problem-based learning,established mathematical model and summarized different research methods. Second let MDVRP transformed into general VRP problom by increasing virtual distribution center. Third using improved genetic algorithm to solve the problom.Finally testing different data sets, analyzing, comparing and summarizing experiment results to prove the validity and scope of the improved algorithm. |
