基于改进蚁群算法的带容量约束车辆路径问题求解

An Improved Ant Colony Optimization for Capacitated Vehicle Routing Problem

随着市场经济快速发展和现代技术的不断演变,现代物流业也得到了空前的发展。在物流配送活动的各个环节中,配送路径优化对企业提高服务质量、降低物流成本、提高经济效益起到至关重要的作用。蚁群优化算法作为群智能算法的典型代表,在路径规划求解中表现出良好的效果。本文研究了带容量约束车辆路径问题(Capacitated Vehicle Routing Problem,CVRP),并采用蚁群优化算法进行优化求解。实验结果表明,蚁群优化算法能够有效地求解带容量约束车辆路径问题。

With the rapid development of market economy and the continuous evolution of modern technology, modern logistics industry has also achieved unprecedented development. In all links of logistics distribution activities, distribution path optimization plays a vital role in improving service quality, reducing logistics costs and increasing economic benefits. As a typical representative of swarm intelligence algorithm, ant colony optimization algorithm shows good results in path planning. This paper studies the Capacitated Vehicle Routing Problem(CVRP), and uses ant colony optimization algorithm to solve it. The experimental results show that the ant colony optimization algorithm can effectively solve the vehicle routing problem with capacity constraints.