Using the Ant Colony Optimization algorithm for the Capacitated Vehicle Routing Problem

P. Stodola, J. Mazal, M. Podhorec, O. Litvaj
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引用次数: 19

Abstract

This paper deals with the application of the Ant Colony Optimization (ACO) algorithm to solve the Capacitated Vehicle Routing Problem (CVRP). The first part presents the basic approach and concept which has been inspired by nature. Next, the basic features and parameters of the algorithm are discussed. Then, a number of experiments are introduced which served to verify the algorithm. We chose Christofides, Mingozzi and Toth's CVRP instances as benchmark problems. The results we obtained are compared with other state-of-the-art algorithms. Next, the improvement of the algorithm is presented. The last part of the paper presents the application of the problem in practice; the primary objective is to plan the distribution of supplies and logistics in the real environment. Finally, the paper summarizes some perspectives of our future work.
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用蚁群优化算法求解有能力车辆路径问题
研究了蚁群优化算法在有能力车辆路径问题中的应用。第一部分介绍了受自然启发的基本方法和概念。其次,讨论了该算法的基本特征和参数。然后,通过实验对算法进行了验证。我们选择Christofides, Mingozzi和Toth的CVRP实例作为基准问题。并将所得结果与其他最先进的算法进行了比较。然后,对算法进行了改进。论文的最后一部分给出了该问题在实践中的应用;主要目标是在真实环境中规划物资和物流的分配。最后,对今后的工作进行了展望。
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