VRPTW:带时间窗的车辆路径问题的计算后勤。 PSO和GA在VRPTW中的应用比较回顾-源码

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带时间窗的车辆路径问题 带时间窗(VRPTW)的车辆路径问题的计算后勤。 求解技术,粒子群优化(PSO)算法和遗传算法(GA)在VRPTW中的应用比较回顾。 解决方案算法 解决方案技术算法基于并根据下面给出的相应参考文献进行。 该算法使用Python 3进行编码。 VRPTW的遗传算法: Ombuki,Beatrice,Brian J. Ross和Franklin Hanshar。 “带有时间窗的车辆路径问题的多目标遗传算法。” 应用智能24.1(2006):17-30。 VRPTW的粒子群优化算法: 龚永杰,张静,刘澳,黄瑞珠,钟HS,史玉华。 使用时间窗优化车辆路径问题:离散粒子群优化方法。 IEEE关于系统,人与控制论的交易,C部分(应用和评论)。 2012年3月; 42(2):254-67。 数据集:所罗门基准数据集 更新的文件: 对于每种算法和各自的指标,可以在以下文件

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