Research On Scheduling Problem For Logistic Center Vehicles

With the rapid development of e-commerce, logistic industry has also experienced a new reform. Intelligent logistics is an important part of it and plays a key role to realize highly effective logistics. In the process of logistics, there are abundant operational and decision-making problems that need to be solved, and logistic vehicle routing problem is one of which. It affects the speed, cost and benefit of delivery directly. We have done some researches and explorations on the interrelated technologies of this problem and developed a simulation system of optimizing scheduling logistic vehicles, which is used to check up the feasibility and validity about the interrelated technologies. This article will examine the different aspects of the technologies as follow:The aim at the characteristics and functions of a logistic vehicle's electronic map, roads' subsection-disposal theory is presented and, a database structure about a road net is proposed at the same time, which is used to store the electronic map. It's very effective to get the topological relationships between distribution destinations and a physical distribution center when we use the road net database. By this means, we devised some city zones' logistic electronic map of NanNing.Logistic vehicle routing problem is combination optimization that is difficult to solve. Due to its NP characteristics, we can't get satisfying results when we use exact approaches and normal heuristic ones. Metaheuristics add fresh power into solving this problem. Genetic algorithms and tabu search algorithms are two representative metaheuristic algorithms, based on which, we constructed an improved genetic algorithm to solve logistic vehicle routing problem, whose improvement is mainly presented at two aspects: on one hand, as tabu mutation operator, tabu search algorithm is inserted into genetic algorithm, which makes the two algorithms bringing out the best in each other. Due to tabu search algorithm's great capacity of climbing hill, genetic algorithm's "premature convergence" phenomenon is effectively avoided and, the result quality is improved. So tabusearch is a beneficial complementarity to genetic algorithm. On the other hand, an improved RouteCrossover operator(RC') is introduced. With the increasing size of a problem, it can obtain general optimality of four indexes on the premise of satisfying every customer's demand: distance, vehicle number, idle time and no-service number, and its performance is superior to PMX and RC.At length, a simulation system of optimizing scheduling logistic vehicles is developed, which is used to validate our improved genetic algorithm and some settlement schemes of electronic map. This system includes vehicle scheduling, loading plan and routing every vehicle, etc. It can manage and schedule logistic vehicles soundly. Experiments have shown that our proposals are correct, feasible and useful.