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A Reinforcement Learning-based Approach to Dynamic Job-shop Scheduling
查看参考文献12篇
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文摘
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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 seldom address machine selection in the scheduling process. Composite rules, considering both machine selection andjob 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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来源
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自动化学报
,2005,31(5):765-771 【核心库】
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关键词
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Reinforcement learning
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composite rules
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mean tardiness
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dynamic job-shop scheduling
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地址
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Shenyang Institute of Automation, Chinese Academy of Sciences, 辽宁, Shenyang, 110016
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语种
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英文 |
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文献类型
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研究性论文 |
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ISSN
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0254-4156 |
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学科
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自动化技术、计算机技术 |
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基金
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国家973计划
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文献收藏号
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CSCD:2085114
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参考文献 共
12
共1页
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