不确定的中断能力 多目的灵活的工作场所通过深度强化学习 基于异质图的学习 自我注意力
概括
本研究介绍了一种先进的深度强化学习 (DRL) 方法,用于灵活的工作场所问题,结合了像员工工作时间这样的现实约束. 这种方法有效地优化了生产时间,成本和延迟.
科学领域:
- 运营研究 运营研究
- 人工智能的人工智能
背景情况:
- 灵活工作场所问题 (FJSP) 研究往往忽视了实际约束.
- 现实的约束包括员工的工作时间和不可中断的运行.
研究的目的:
- 为灵活的工作场所问题 (FJSP) 开发一种改进的深度强化学习 (DRL) 方法.
- 为了应对现实的限制,如员工工作时间和不可中断的运营.
- 为了同时优化 makespan,总成本和总延迟.
主要方法:
- 使用端到端的多决策智能机构近距离政策优化 (m-PPO).
- 嵌入一个异质图自我注意神经网络 (HGAN) 用于特征提取.
- 采用规则驱动的工作决策代理和数据驱动的操作机器 (O-M) 对决策代理.
主要成果:
- HGAN模型有效地从异构图中提取特征.
- 代理人将特定问题的知识纳入决策.
- 网络生成的,自动更新的权重参数可以同时优化多个目标.
结论:
- 拟议的DRL框架有效地处理了FJSP中的现实约束.
- 该方法在优化产量,成本和延迟方面表现出卓越的性能.
- 数字实验验证了改进的DRL方法的有效性.
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