通过双重注意力基于网络的强化学习实现灵活的工作车间安排
IEEE transactions on neural networks and learning systems
|September 11, 2023
概括
本研究引入了一个新的深度学习框架,使用注意力模型和强化学习来解决复杂的灵活工作室调度问题 (FJSP). 该方法显著提高了制造业操作的调度效率和解决方案质量.
科学领域:
- 运营研究 运营研究
- 人工智能的人工智能
- 制造系统工程 制造系统工程
背景情况:
- 灵活的工作车间调度问题 (FJSP) 具有显著的复杂性,因为操作可以在多台机器上处理.
- 现有的深度强化学习 (DRL) 方法对于FJSP,虽然有希望,但与精确方法相比,往往会产生低于最佳质量的解决方案.
- 需要先进的方法来有效地捕捉FJSP中复杂的操作机器关系.
研究的目的:
- 开发一种新的端到端学习框架,以解决灵活的工作场所调度问题 (FJSP).
- 通过将自我注意机制与深度强化学习相结合,增强FJSP的决策能力.
- 提高复杂制造日程安排解决方案的质量和可扩展性.
主要方法:
- 提出了一种双重注意网络 (DAN),结合了相互连接的操作和机器注意区块,用于深度特征提取.
- DAN利用自我注意力,精确地模拟操作和机器之间的复杂关系.
- 深度强化学习 (DRL) 用于可扩展的决策,以从DAN中提取的特征为指导.
主要成果:
- 拟议的DAN-DRL框架在合成和基准FJSP数据集上明显优于传统的优先调度规则 (PDR) 和现有的最先进的DRL方法.
- 该方法在特定场景中实现了与精确方法 (例如OR-Tools) 相当的解决方案质量.
- 在大规模和现实世界未见的FJSP实例上展示了有利的概括能力.
结论:
- 新的端到端学习框架有效地解决了FJSP当前DRL方法的局限性.
- 双重注意网络成功地捕捉了复杂的生产动态,从而产生了高质量的调度决策.
- 该方法为复杂的制造日程安排提供了可扩展和有效的替代方案,接近精确方法的性能.
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