Vehicles Based On Ant Colony Algorithm To Optimize The Scheduling System

With the growth of the pressure of market and diversification of demand of customs, many corporations have reappraised their vehicle routing strategy in order to use the minimum transport costs to make maximum interests. Vehicle routing problem is a very typical optimization problem. There have been a lot of optimization methods to solve the vehicle routing problem at present, but most of these methods have some limitation. As a novel simulated evolutionary algorithm, Ant Colony Optimization (ACO) has many merits as positive feedback, robust, parallel compute, coordination, so it is very suitable to solve vehicle routing problem and accords with the tendency that vehicle routing algorithm evolves into intelligent and simulated evolutionary.To overcome the default of stagnation and convergence speed slow, an improved ACO strategy is present. We proposed Candidate City Table and added Saving Heuristics Information into the state transition rule of ACO to reduce the computing costs; to overcome the default of Ant Colony System (ACS) and Max-Min Ant System (MMAS), a three level pheromone update algorithm and 2-opt local search strategy is presented; to solve vehicle routing problem better, the paper proposed an adaptive ant colony algorithm; and the simulation result shows that the improved ant colony algorithm can search the better solution more quickly; with the intelligent developing platform, based-on improved ant colony algorithm, we developed an intelligent vehicle routing system. The system can help customer to analyze and optimal the vehicle routing problem, greatly reduced the cost of company, and make some contribution to the development of vehicle routing system in the theoretical research and the applied value.