三个学习策略 粒子群集优化用于空地协作物流运输调度问题 采集和交付 考虑客户优先级
Yuanhang Qi1, Haoran Jiang1,2, Gewen Huang3
1School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan, 528402, China.
Scientific reports
|December 7, 2025
概括
本研究介绍了一种空地物流模型,以实现高效的交付和接送,最大限度地降低成本. 拟议的三学习策略粒子优化算法与传统方法相比,大大降低了总旅行费用.
科学领域:
- 物流和供应链管理的物流和供应链管理.
- 运营研究 运营研究
- 人工智能的人工智能
背景情况:
- 传统的物流模式在优化复杂的交付和接送场景方面面临着挑战.
- 将无人驾驶飞行器 (UAV) 与地面车辆相结合,有可能提高效率.
- 客户优先级和成本因素在物流调度中至关重要.
研究的目的:
- 提出一个新的空中-地面协作物流分销模式,用于交付-提取前情景.
- 开发一个智能优化算法来解决空地协作物流运输调度问题,考虑客户优先次序 (ALTSPPDCP).
- 通过考虑客户优先级,车辆成本和无人机成本,最大限度地降低总旅行成本.
主要方法:
- 空地协作物流运输调度问题与顾客优先考虑的接送和交付 (ALTSPPDCP) 的制定.
- 设计一个多层,多阶段的编码和解码策略.
- 实施三学习策略粒子集群优化 (TSLPSO) 算法,结合上升顺序排序和动态细分方法.
主要成果:
- 车辆-无人机协作方案在50个节点的场景中实现了14.86%的总成本降低,与50节点的单车方案相比.
- 与粒子群优化 (PSO) 相比,TSLPSO算法表现出卓越的性能,降低了39.99%的成本,与随机粒子群优化 (RPSO) 相比,降低了27.94%.
结论:
- 空地协作物流在复杂的交付场景中显著降低了运营成本.
- 提议的TSLPSO算法是解决ALTSPPDCP的有效和高效方法.
- 与传统方法相比,智能优化算法为物流调度问题提供了卓越的解决方案.
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