Research And Application Of Hybrid Ant Colony Optimization For Vehicle Routing Problem

Under the impetus of scientific and technological progress, especially the information technology on behalf of networks the wide range of applications in production, circulation and consumer field, economic globalization was speeding up gradually in the past20years. With the development of science and technology and the strengthening of global economic integration, logistics enterprises in China are facing unprecedented opportunities and challenges. In the environment of more severe of current economic situation and increasingly competitive market, developing modern logistics which is an advanced organization and management technique has become an urgent need for business survival and development. Effective logistics management is widely considered to be another way to increase profits of companies in addition to lower material consumption and improve labor productivitly. so how to improve the effectiveness of logistics management has become the focus to logistics enterprises.Logistics distribution is a very important part of logistics management. Efficient logistics distribution can reduce transport costs and increase efficiency. And the vehicle routing problem is the most in need to be resolved. Researchers had done a number of studies address this issue and also raised a lot of algorithms such as genetic algorithms, simulated annealing, and ant colony algorithm. These algorithms has its own advantages and disadvantages.But the vehicle routing problem is a NP-hard problem, there is difficult to effectively solve this problem only use a single algorithm. Therefore, this paper raises a hybrid ant colony optimization based on improving genetic algorithm and ant colony algorithm that under a detailed analysis of the characteristics of them. The basic idea of this algorithm is use the advantages of genetic algorithm which is mass, global, randomized, and fast search to generate an initial feasible solution firstly, and translate them into the ant colony algorithm for the initial pheromone distribution, and then use the advantages of ant colony algorithm which is positive feedback and fast convergence to obtain the optimal solution. This algorithm can effectively avoid the precocious convergence phenomenon of genetic algorithm and the shortcomings of the preliminary search slowly of ant colony algorithm, so it can improve the ability to solve problems.In order to verify the validity of the algorithm, this paper programming experiments in the Visual C++6.0environment, and the experimental results are compared with the results obtained by other algorithms. The result show that the hybrid ant colony optimization proposed in this paper can be an effective way to solve the vehicle routing problem and it can achieve satisfactory solution. Finally, this paper develops a logistics route optimization system in use of GIS technology and for practical problems and applies the hybrid ant colony optimization to the actual vehicle routing optimization model to obtain the optimal solution..