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A Reinforcement Learning-based Approach to Dynamic Job-shop Scheduling
Alternative Title一种基于强化学习的作业车间动态调度方法
Wei YZ(魏英姿); Zhao MY(赵明扬)
Source Publication自动化学报
Indexed ByEI ; CSCD
EI Accession number2005429423806
Contribution Rank1
Funding OrganizationSupported by 973 program of P.R.China (2002CB312200)
KeywordReinforcement Learning Composite Rules Mean Tardiness Dynamic Job-shop Scheduling
AbstractProduction 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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Document Type期刊论文
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GB/T 7714
Wei YZ,Zhao MY. A Reinforcement Learning-based Approach to Dynamic Job-shop Scheduling[J]. 自动化学报,2005,31(5):765-771.
APA Wei YZ,&Zhao MY.(2005).A Reinforcement Learning-based Approach to Dynamic Job-shop Scheduling.自动化学报,31(5),765-771.
MLA Wei YZ,et al."A Reinforcement Learning-based Approach to Dynamic Job-shop Scheduling".自动化学报 31.5(2005):765-771.
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