The Research Of Route Optimizing Of Logistics Based On Multi-level Ant Colony Algorithm

With rapid development of express industry, the reasonable arrangement of logistics route plays an important role in the efficient operation of express companies. Logistics route optimization, which just considers about the shortest time or the least cost, can’t meet the needs of express companies. In this paper, timeliness and economy are both considered. This paper optimizes logistics route from the point of taking both time and cost into account. The diversity of intercity transport mode and inner-city transport modes causes that the cost of time is not proportional to the economic cost. Therefore, the research of optimization of logistics route considering both logistics time and cost can help the express companies to develop better.The artificial intelligent algorithms are designed for solving complex problems which are not linear and have a unique solution. Ant colony algorithm is a new developed artificial intelligent algorithm, which is depended on heuristic function and the rule of updating the pheromone. This paper takes the problems of optimization of logistics route as the research object. In view of the special nature of the goal of optimizing logistics route considering logistics time and cost, a multi-level ant colony algorithm is proposed, which adopts different state transition strategies and heuristic functions in different levels.The experiment data comes from a famous express company. The number of real express data used in the experiment is 38,752,378. The time of data spans from May 10, 2014 to February 6,2015, which really covers a broad range. It makes sure that the results are more realistic and can provide significant practical reference value for the express companies.The main work is as follows:(1) Research of optimization on logistics delivery route is introduced by consulting large amount of literature. Then we analyze the feature of delivery network and compare advantages and disadvantages of different transport modes. Then the factors that affect the path selection of express companies are described. All that work provides theoretical support for the validity of optimization of logistics route.(2) In order to make up for the weakness of basic ant colony algorithm which can only solve the problem of a single optimization, a multi-level ant colony algorithm is proposed. On the basis of the basic ant colony algorithm, the nodes are divided into different levels. Then different state transition strategies and heuristic functions are applied in different levels. Meanwhile, the rule of updating the pheromone is improved in order to improve the convergence speed and the global search capability.(3) The original data, which is chaotic, is pretreated so as to obtain the data which can be used for routing optimization more convenient. The multi-level ant colony algorithm is realized by programming. The result indicates that this improved algorithm is effectively and feasibly.