Vehicle Routing Optimization Problems In Logistics Based On Ant Colony Algorithm

Vehicle routing optimization in logistics is one of the most critical parts in logistics. It can improve the economic benefit and realize the scientific process of logistics. The study of vehicle scheduling optimization theory and method definitely has its significant importance. It can enhance the intensive development of logistics; construct integrated logistics system and modern scheduling system of command; develop intelligent traffic transportation system and be a basic platform of electronic business. Ant colony algorithm is a new fuzzy logic algorithm enlightened by the foraging behavior of ants. Great effects have been achieved by applying this ant algorithm to vehicle routing problem. The main theme of this thesis is to improve the existing ant algorithm and make it suitable to various kinds of vehicle routing optimization problem and gain high quality results. The main tasks are the following four aspects:1. Give a specific description of VRP, classifies it and gives out the meaning, limitation and application scope. Then, establish a model for the VRP with capacity limitation and expatiate a number of solving methods.2. Make further study of basic principle of ant algorithm, overall analysis and set up mathematic model for it, including transfer probability calculation, time and space complexity, realization problem, advantages and disadvantages and so on.3. Conduct a deep research on capacitated vehicle routing problem, and present an improved ant colony algorithm to solve it. In this method, we adopt the strategy combined defined selection and random selection, big ant number strategy, mean initial distributing method and a strategy of feasible approximate solution and so on. So we can get the feasible solutions of the CVRP problem.4. Study the VRP with time windows and establish its mathematic model. Then we present an improved ant colony algorithm, including the new selection strategy of the construction path, local pheromone update method, and local optimization based on 2-opt method, global pheromone update method and so on. A strategy of feasible approximate solution is put forward. In the strategy, three types of branch circuits are established and the ownership of skipped clients is processed to make sure a feasible solution is created in each time of iteration. In this way, feasible solution is created in each time of iteration.