相关实验视频
Updated: Sep 10, 2025

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Operation of the Collaborative Composite Manufacturing CCM System
Published on: October 1, 2019
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一个端到端的框架,用于节能级联双工厂协作调度与配对操作
IEEE transactions on cybernetics
|August 26, 2025
概括
这项研究引入了节能级联双车间协作调度问题与交配操作 (ECDCSP-M). 基于图形的深度强化学习方法有效优化复杂的生产时间表,表现出强大的性能.
科学领域:
- 运营研究
- 工业工程
- 人工智能
背景情况:
- 现代生产涉及复杂的多阶段过程,需要有效的计划.
- 相互依赖的车间和集成的主/子订单 (交配操作) 提出了独特的调度挑战.
- 现有的调度模型往往忽视了级联双店环境中的协作优化.
研究的目的:
- 制定和解决节能级联双车间协作调度问题 (ECDCSP-M).
- 开发一个混合整数线性编程 (MILP) 模型来准确表示问题.
- 设计一个基于端到端图的深度强化学习 (GDRL) 方法,以优化调度.
主要方法:
- 使用混合整数线性编程 (MILP) 制定 ECDCSP-M.
- 开发基于图形的深度强化学习 (GDRL) 框架.
- 构建一个双门异构图和一个异构图神经网络 (HGNN),具有三阶段嵌入机制.
主要成果:
- 提出的GDRL方法有效地模拟复杂的相互依赖关系,包括交配操作.
- 这种方法在各种复杂的问题上表现出强大的概括能力.
- 在级双工厂系统中实现了对具有挑战性的调度场景的强大解决方案.
结论:
- 开发的GDRL方法为级双车间系统的节能协作调度提供了有效的解决方案.
- 不同质的图形结构和HGNN捕捉复杂的系统状态和关系.
- 这项研究提升了配对操作的复杂制造环境的调度优化.
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