在多次订单干扰下使用深度强化学习进行动态工作车间调度.
Zhiyuan Sun1,2, Wenmin Han1, Longlong Gao1
1School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China.
Science progress
|April 22, 2025
概括
本研究介绍了独立近接政策优化 (IPPO) 对于动态的工作车间安排,有效地减少制造中的延误和完成时间. 新的多代理深度强化学习方法在复杂场景中表现优于现有方法.
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:
- 由于多次订单干扰,动态工作车间的安排是复杂的.
- 现有的方法在实时调整和多目标优化方面扎.
研究的目的:
- 开发一种新的方法,用于动态的工作车间安排使用多代理深度强化学习.
- 为了最大限度地减少总延迟和制造环境中的 makespan.
主要方法:
- 利用独立近接政策优化 (IPPO),一个多代理深度强化学习算法.
- 使用五通道二维图像表示系统状态.
- 设计了一个奖励功能,专注于减少总迟到和 makespan.
主要成果:
- 基于IPPO的方法在72个场景中表现出比传统的深度强化学习算法和调度规则更好的性能.
- 在最大限度地减少总延迟和 makespan.方面取得了显著的改进.
- 展示了强大的优化和探索能力.
结论:
- 拟议的IPPO方法为动态制造环境中的复杂,多目标调度提供了有希望和有效的解决方案.
- 突出了多代理深度强化学习的潜力,以应对现实世界的调度挑战.
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