| A Reinforcement Learning-based Approach to Dynamic Job-shop Scheduling |
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| 引用本文： | WEI Ying-Zi~(1,2) ZHAO Ming-Yang~1 ~1(Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016)~2(Shenyang Ligong University,Shenyang 110168). A Reinforcement Learning-based Approach to Dynamic Job-shop Scheduling[J]. 自动化学报, 2005, 0(5) |
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| 作者姓名： | WEI Ying-Zi~(1 2) ZHAO Ming-Yang~1 ~1(Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016)~2(Shenyang Ligong University Shenyang 110168) |
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| 作者单位： | WEI Ying-Zi~(1，2) ZHAO Ming-Yang~1 ~1(Shenyang Institute of Automation，Chinese Academy of Sciences，Shenyang 110016)~2(Shenyang Ligong University，Shenyang 110168) |
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| 基金项目： | Supported by 973 program of P.R.China (2002CB312200) |
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| 摘 要： | Production scheduling is critical to manufacturing system.Dispatching rules are usually applied dynamically to schedule (?)he 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 concep- tion 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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A Reinforcement Learning-based Approach to Dynamic Job-shop Scheduling |
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| WEI Ying-Zi. A Reinforcement Learning-based Approach to Dynamic Job-shop Scheduling[J]. Acta Automatica Sinica, 2005, 0(5) |
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| Authors: | WEI Ying-Zi |
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| Affiliation: | WEI Ying-Zi~ |
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| Abstract: | Production scheduling is critical to manufacturing system.Dispatching rules are usually applied dynamically to schedule (?)he 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 concep- tion 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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| Keywords: | Reinforcement learning composite rules mean tardiness dynamic job-shop scheduling |
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