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    科学领域:

    • 运营研究 运营研究
    • 计算机科学 计算机科学
    • 物流管理物流管理

    背景情况:

    • 殖民地优化 (ACO) 由于其分布式性质,被用于即时交付调度.
    • 目前的ACO方法在保持动态物流状态的交付效率方面面临挑战.

    研究的目的:

    • 为了提高殖民地优化 (ACO) 的性能,以实现即时交付订单安排.
    • 开发一个自适应的ACO算法,结合实时物流特征 (AACO-RTLFs).

    主要方法:

    • 从事件,空间和时间维度提取特征,以定义实时物流状态.
    • 开发一种适应性即时交付模型,包括客户可接受的交付时间,紧急订单标志和天气条件.
    • 基于提取的关键物流因素调整参数的自适应ACO算法的建议.

    主要成果:

    • 适应性ACO算法 (AACO-RTLF) 有效地改善了即时交货订单的安排.
    • 使用Gurobi解答器进行的数值实验验证实了算法在经典数据集上的有效性.
    • 与现有的最先进的算法相比,AACO-RTLF在即时交付场景中表现出更高的性能.

    结论:

    • 拟议的AACO-RTLF算法为即时交货订单安排提供了显著的优势.
    • 实时物流功能集成和自适应参数调整对于优化交付效率至关重要.
    • 适应式即时交付模型有效地考虑了影响交付时间的关键因素.