An Improved Ant Colony Optimization For Vehicle Routing Problem With Time Windows

The logistics industry is an important pillar of the development of the modern economy, especially in the Internet age, the level of logistics becomes an important capability for enterprises to gain competitive advantage. But the high logistics costs hinder large-scale, large-scale and efficient goods circulation. In 2013, the total social logistics profit is (?)197.8 trillion, the annual logistics cost is (?)10.2 trillion; the ratio of total social logistics costs to GDP was 18%. This makes that to enhance the level of logistics has become an important issue for the business community and Academia.Based on the mode of operation of the logistics, academics proposed vehicle routing problem (VRP) and an extension:the problem vehicle routing problem with time windows (VRPTW). This paper focuses on the vehicle routing problem with time windows, VRPTW has been proved to be NP-hard problem, when the problems is large-scale, it will be difficult to get the exact solution of the problem. How to get a relatively satisfactory solution through a small amount of calculation is the focus of academic research. Heuristic algorithm is an effective research way to solve the current problem of large-scale VRPTW, ant colony algorithm in this paper is such a case.Heuristic algorithm is based on the true colony of ants foraging behavior of bionic algorithm, have good robustness for solving combinatorial optimization problems. This paper presents an improved ant colony algorithm based on MMAS, the algorithm fully exploit the existing data from the state transition probability, pheromone update other aspects of MMAS to improve on the quality of the solution speed reconciliation achieved a better balance. The parameters were optimized by Uniform Design and the validity and feasibility of the algorithm was demonstrated by using the Solomon baseline data set.