Research On Ant Colony Algorithms For Optimization Of Vehicle Routing Problems

Under the global context of economic integration, With the rapid development of China's economy, Logistics, which is known as "The third profit source", has made rapid progress in our country. Distribution of logistics activities associated directly with the consumer-related sectors and accounted for a very high percentage of all the cost of logistics. Therefore, how to arrange reasonableness distribution lines imposes a direct impact on the costs of business. Under the promise of meeting diverse user requirements, How to use existed resources effectively to reduce the corporate vehicle scheduling operating costs and then bring greater profits to the enterprise is the goal of the logistics industry, and also is the focus of attention among researchers.Distribution problem is one of the most important issues of the logistics industry, which is essentially a vehicle routing problem(VRP), VRP has high computational complexity, and is NP-hard problem. With the expansion of scale of the problem, traditional optimization algorithm Based on the deterministic encountered difficulties in solving combinatorial optimization problems, and then people were enlightened in the bionics, and put out a number of heuristic intelligent optimization algorithms which provides a new idea on solving complex combinatorial optimization problems.Ant colony algorithm is a bionic optimization algorithm which is summed up by human to observe the process of ant foraging. It is just in about ten years of development history to show its vitality. It has been successfully applied to solve combinatorial optimization problems such as the traveling salesman problem (TSP), job-shop scheduling problem (JSP), and vehicle routing problem (VRP). Ant colony algorithm, which is a new bionic optimization algorithm, is widely used because of its distributed computing, self-organization and the nature of positive feedback. But the long searching time, and easy to fall into local optimal solution is also the fatal shortcomings of the basic ant colony algorithm. For this problem, this paper proposes an improved ant colony algorithm based on studying the genetic algorithm as we called G-ACA. The experimental results show that G-ant colony algorithm has better overall performance in convergence speed of reconciliation.To verify the performance of the algorithm, we executed experiments under VC6.0, and develop a application software response to the TSP problem and the VRP problem. Experimental results show that we can get satisfactory results using improved ant colony algorithm on solving VRP and TSP.And finally, for the rapid development of logistics and distribution industry, we have put forward the idea on developing logistics and distribution management system for dispatching vehicles, and analysis of the current problems, and provide a theoretical support and practical reference for the subsequent researchers.