一个基于可扩展图形变压器的强化学习控制框架,用于大规模的模糊工作室调度问题
概括
本研究介绍了一种新的图形变换器近距离政策优化 (GT-PPO) 算法,以解决复杂的模糊工作室调度问题. GT-PPO算法在各种规模上展示了强大的性能,为大规模的调度挑战提供了高效的解决方案.
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:
- 工作坊调度问题 (JSSP) 是一个具有计算挑战性的NP-hard问题.
- 现实的JSSP变体通常涉及不确定性,例如模糊的处理时间,使优化复杂化.
- 现有的方法在与大型JSSP实例固有的异质性和远程依赖性作斗争.
研究的目的:
- 解决模糊的工作车间安排问题 (JSSP),目的是尽量减少最大完成时间.
- 提出一种新的算法,即用图形转换器 (GT-PPO) 进行近接策略优化,用于解决这种复杂的调度变量.
- 增强状态和动作表示,以提高调度性能.
主要方法:
- 该研究使用近接政策优化 (PPO) 作为核心的强化学习框架.
- 图形转换器 (GT) 架构用于捕捉模糊断层图形和远程依赖中的复杂关系.
- 为了高效处理大规模的JSSP实例,GT的计算复杂性减少到O (n).
主要成果:
- 建议的GT-PPO算法在单次培训课程后,在生成和公共JSSP实例的各种规模中显示出强大的稳定性.
- 在大规模的DMU和Taillard公共数据集上观察到异常强度.
- 该算法有效地处理模糊处理时间,并优化最大完成时间.
结论:
- GT-PPO算法为大规模模糊工作室调度问题提供了有效和高效的解决方案.
- 图形变压器的集成解决了传统图形神经网络在处理JSSP复杂性的局限性.
- 该模型的稳定性和效率为复杂的调度环境中的实际应用铺平了道路.
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