基于蚁群算法的物流配送路线优化研究

Tianshi Liu, Yike Yin, Xiaobo Yang
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引用次数: 2

摘要

针对路线优化问题,以总运输成本最小、总配送路线最短为目标,构建了多目标优化路线分配模型。为了克服蚁群优化算法搜索时间过长、容易陷入局部最优的缺点,采用自适应变化值的方法改进蚁群信息素挥发系数,提高了蚁群优化算法的适应性。因此,采用改进的蚁群算法来解决物流配送路线优化问题。实验结果表明,改进的蚁群优化方法可以得到较好的物流配送路线方案,为提高物流企业的经济效益提供了有价值的参考信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Logistics Distribution Routes Optimization Based on ACO
Aiming at the problem of route optimization, a multi-objective optimized route distribution model is constructed with the goal of the smallest total transportation cost and the shortest total delivery route. In order to overcome the shortcomings of ant colony optimization, such as too long search time and easy to fall into local optimum, the ant colony pheromone volatilization coefficient is improved by the method of adaptive change value, which improves the adaptability of ant colony optimization. Therefore, the improved ant colony optimization is used to solve the logistics distribution route optimization problem. The experimental results show that the improved ant colony optimization can obtain a better logistics distribution route plan, which provides valuable reference information for improving the economic benefits of logistics enterprises.
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