Research Of Vehicle Routing Problem Based On Ant Colony Algorithm

Modern transportation vehicle routing process is complexity and changeful, How to solve the vehicle routing problem and how to optimize the cost in the transportation of products, so as to satisfy the various demands of customers and make the enterprise profitable, these questions had appealed to many managers and researchers. But the solutions of existing mathematics methods are not suitable to solve these problems which lack of scientific theory to guide. In order to solve these problems, we usually use the modern optimization algorithm to help us to make scientific decisions, and get maximum profits, minimum cost in the transportation system. In this paper, modern vehicle routing problem is concluded and analysis, discussed the characteristics of vehicle routing problem, sort of problem, problem models and the common used algorithms to solve vehicle routing problem now are briefly introduced.The ant colony algorithm (ACA) is a new method for optimization, which is based on the research on the ant colony. It iterates to the best answer according to selective strategy and pheromone which produced by ant individuals. Generally solution of ant algorithm is described in this paper, presented a application example, and analysis the problems of early converged or stagnation of traditional algorithms. According to the improvement of update coefficient, pheromone and selective strategy, accelerate the convergent speed and enhance the searching ability.Based on the modern logistic technology, especially the vehicle scheduling and the ant algorithm theories, this paper make a research in theories, methods and models aiming at the present problems. In this paper, a second-order algorithm based on Sweep and ant colony algorithms is adopted to solve the optimize scheduling of logistic transportation vehicle. To search new solution to general vehicle scheduling problem (namely vehicle scheduling problem without time window), vehicle scheduling problem with time window, and make simulations of the algorithms with procedures, combined the 4S integrative intelligent vehicle system to discuss the application in real vehicle scheduling of this algorithm.The conclusion of this paper is valuable to the study of ant colony algorithm, andhas real theory guide significance and application valuable to establish modern logistic transportation vehicle optimization scheduling system.