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一种强化学习方法,用于稳定调度换流量车间的稳定调度
Tao Zhou1, Liang Luo1, Shengchen Ji1
1Ministry of Education Key Laboratory of High Performance Ship Technology, Wuhan University of Technology, Wuhan 420100, China.
Biomimetics (Basel, Switzerland)
|October 27, 2023
概括
本研究引入了深度强化学习方法,用于变量流车间调度问题 (PFSP),以尽量减少完成时间. 与现有方法相比,这种新的方法显著减少了产量和计算时间.
科学领域:
- 运营研究 运营研究
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 变量流车间调度问题 (PFSP) 是制造业中常见的一个复杂的组合优化挑战.
- 传统的日程安排方法与公共安全政策的动态性质作斗争.
研究的目的:
- 开发一个端到端的深度强化学习 (DRL) 方法论,用于FSP.
- 为了尽量减少最大完成时间 (makespan) 在流动店环境中.
主要方法:
- 模拟PFSP作为马尔科夫决策过程,定义状态,行动和奖励.
- 使用断层图表来表示PFSP状态信息.
- 采用图形异态网络策略,通过近接策略优化进行训练.
主要成果:
- 在随机和Taillard基准实例中,DLR方法的表现优于六种基线方法.
- 在makepan中实现了显著的减少,节省了高达183.2小时 (随机) 和188.4小时 (Taillard).
- 计算时间缩短至18.70秒 (随机) 和18.16秒 (泰拉德).
结论:
- 拟议的DRL方法为FSP提供了一个优越的解决方案.
- 在最大限度地减少制作时间和提高调度问题的计算效率方面表现出有效性.
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