在集装箱码头使用图形理论和DDQN进行卡车调度协作优化
Shu Cheng1, Qianyu Liu2,3, Heng Jin4
1School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, China.
Scientific reports
|February 26, 2025
概括
这项研究引入了一种新的算法,用于优化集装箱码头的卡车调度. 深度双重Q网络 (DDQN) 方法显著减少设备等待时间,并提高整体港口效率.
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:
- 集装箱码头是全球贸易和物流的关键枢纽.
- 卡车计划效率低下导致卡车利用率低,设备等待时间长,阻碍港口运营.
研究的目的:
- 为集装箱码头开发一个优化的卡车调度策略.
- 尽量减少终端设备的最大完成时间,提高整体效率.
主要方法:
- 一个基于图形理论的容器终端模拟模型被开发出来.
- 为使用深度双重Q网络 (DDQN) 的卡车车队提出了一种协作调度算法.
- 该DDQN算法结合了五个启发式规则,精细的状态特征和奖励函数.
主要成果:
- 该DDQN算法始终确定了最佳的调度策略.
- 它的性能优于现有的启发式规则和深度Q网络 (DQN) 算法.
- 观测到码头起重机等待时间和设备完成时间的显著减少,以及改善卡车利用率.
结论:
- 提出的基于DDQN的算法有效地优化了集装箱码头的卡车调度.
- 这导致港口效率和资源利用率的大幅提高.
- 这种方法为增强集装箱码头运营提供了一个有希望的解决方案.
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