Ant Colony Algorithm Is Applied Research In The Urban Distribution

As logistics is playing an increasing important role in world economy, to enhance the industry of city delivery is becoming one of the most critical strategies to achieve the fast economical development of the cities in China. In order to tackle effectively the key management difficulty of the companies specializing in goods dispatching and make good use of their logistic resources, many of these companies have gradually established various kinds of information management systems based on database. However, most of these systems being used at present are on the basis of traditional mathematical models and arithmetic, whose natural limitations lie in their slow speed of solving and lack of convergence. Therefore, they can't provide efficient solutions to the key management difficulty—the route optimization of delivery in dispatching.The ant colony algorithm, enlightened by the collective behaviour of the ants in real nature, proposes a collection-based simulated evolutionary algorithm and is initially applied to solve the well-known TSP problem. It adopts a distributed and parallel computing mechanism, has the characteristics of self-organization and regenerative feedback, and is easy to work with other methods, which combines to give it a priority in solving problems of optimization.After a deep study and analysis of the above arithmetic and the key management difficulty, the paper establishes a mathematical model based on the ant colony algorithm with the purpose of optimizing the route of city delivery in dispatching, and it also realizes the arithmetic. The experiments that follows show that the model can provide a better solution to the goods dispatching in cities, and it introduces a new approach to the business management of the logistic companies. Meanwhile, this paper also presents, according to the model, a solution to combine the arithmetic and information management systems of relevant enterprises. It is a good try to introduce intelligent algorithm into business management and further build a intelligent dispatching system.