一种基于新方法的强化学习,具有深度时差网络,用于灵活的双店调度问题
Xiao Wang1, Peisi Zhong2, Mei Liu3
1College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, 266590, China.
Scientific reports
|April 19, 2024
概括
一个新的深度时间差强化学习算法有效地解决了灵活的双工厂调度问题 (FDSSP),最大限度地减少了生产时间. 这种方法通过优化复杂的生产环境中的任务安排来提高制造效率.
科学领域:
- 运营研究 运营研究
- 人工智能的人工智能
- 制造业 工程 制造工程
背景情况:
- 灵活的双工厂调度问题 (FDSSP) 整合了工厂和装配厂的复杂性.
- 在制造业中,相关任务之间的调度关联带来了重大挑战.
研究的目的:
- 开发一种有效的算法,以尽量减少FDSSP中的 makespan.
- 在灵活的制造系统中解决复杂的任务安排协会.
主要方法:
- 制定了FDSSP作为一个包含组装约束的数学模型.
- 将问题转化为直接战略选择的马尔科夫决策过程.
- 利用一个深度神经网络,有十个状态特征和八个用于决策的构建式启发式.
- 实施了深度时间差强化学习框架.
主要成果:
- 与现有方法相比,拟议的算法表现出优越的性能.
- 有效地减少了对灵活的双店日程安排问题的制造量.
- 通过广泛的比较实验来验证.
结论:
- 开发的深度时间差强化学习算法为FDSSP提供了强大的解决方案.
- 这种方法对提高制造业效率具有实际意义.
- 在灵活制造中成功地解决了复杂的调度协会.
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