相关实验视频
基于层次深度强化学习的动态物流调度的多目标优化.
1College of Mechanical, University of Science and Technology, Beijing, 100083, China. cassjt@126.com.
Scientific reports
|September 29, 2025
概括
本研究引入了一个新的深度强化学习框架,以优化复杂的物流调度. 该方法通过在动态环境中平衡竞争目标来提高效率并降低成本.
科学领域:
- 运营研究 运营研究
- 人工智能的人工智能
- 物流管理物流管理
背景情况:
- 现代物流调度面临着由于复杂性,不确定性和竞争目标的挑战.
- 传统的优化和强化学习方法对于动态,多目标的物流问题是不够的.
研究的目的:
- 提出一个新的层次深度强化学习 (DRL) 框架,用于动态物流调度的多目标优化.
- 解决现有方法在处理复杂性,不确定性和竞争目标方面的局限性.
主要方法:
- 实现了两级层次的DRL架构,其中包括高级战略规划和低级战术执行网络.
- 开发了一个帕雷托最佳奖励机制,具有适应权重,以平衡时间,成本和服务质量.
- 利用时间抽象来提高样本效率并处理稀疏的奖励.
主要成果:
- 实现了显著的改进:18.4%的服务质量,15.2%的订单履行时间缩短,运营成本降低7.8%.
- 与最先进的方法相比,在各种物流数据集上表现出卓越的性能.
- 产生高质量的帕雷托最佳解决方案,有效地平衡竞争目标.
结论:
- 拟议的分层DRL框架为多目标动态物流调度提供了强大的解决方案.
- 该框架在动态环境中表现出色,提供战略连贯性和战术适应性,几乎实时的决策.
- 这种方法有效地平衡了相互竞争的目标,改善了整体物流运营.
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