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一种基于强化学习的作业车间动态调度方法

魏英姿 赵明扬

魏英姿, 赵明扬. 一种基于强化学习的作业车间动态调度方法. 自动化学报, 2005, 31(5): 765-771.
引用本文: 魏英姿, 赵明扬. 一种基于强化学习的作业车间动态调度方法. 自动化学报, 2005, 31(5): 765-771.
WEI Ying-Zi, ZHAO Ming-Yang. A Reinforcement Learning-based Approach to DynamicJob-shop Scheduling. ACTA AUTOMATICA SINICA, 2005, 31(5): 765-771.
Citation: WEI Ying-Zi, ZHAO Ming-Yang. A Reinforcement Learning-based Approach to DynamicJob-shop Scheduling. ACTA AUTOMATICA SINICA, 2005, 31(5): 765-771.

一种基于强化学习的作业车间动态调度方法

详细信息
    通讯作者:

    魏英姿

A Reinforcement Learning-based Approach to DynamicJob-shop Scheduling

More Information
    Corresponding author: WEI Ying-Zi
  • 摘要: Production scheduling is critical to manufacturing system. Dispatching rules are usually applied dynamically to schedule the job in a dynamic job-shop. Existing scheduling approaches sel- dom address machine selection in the scheduling process. Composite rules, considering both machine selection and job selection, are proposed in this paper. The dynamic system is trained to enhance its learning and adaptive capability by a reinforcement learning (RL) algorithm. We define the conception of pressure to describe the system feature. Designing a reward function should be guided by the scheduling goal to accurately record the learning progress. Competitive results with the RL-based approach show that it can be used as real-time scheduling technology.
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出版历程
  • 收稿日期:  2004-08-12
  • 修回日期:  2005-06-26
  • 刊出日期:  2005-09-20

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