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Pattern driven dynamic scheduling approach using reinforcement learning
Wei YZ(魏英姿); Jiang, Xinli; Hao, Pingbo; Gu KF(谷侃锋)
Department现代装备研究室
Conference Name2009 IEEE International Conference on Automation and Logistics, ICAL 2009
Conference DateAugust 5-7, 2009
Conference PlaceShenyang, China
Source PublicationProceedings of the 2009 IEEE International Conference on Automation and Logistics, ICAL 2009
PublisherIEEE
Publication PlaceNEW YORK
2009
Pages514-519
Indexed ByEI ; CPCI(ISTP)
EI Accession number20094812516950
WOS IDWOS:000291503400097
Contribution Rank2
ISBN978-1-4244-4795-4
KeywordReinforcement Learning Contract Net Protocol (Cnp) State Pattern Dynamic Scheduling
AbstractProduction scheduling is critical for manufacturing system. Dispatching rules are usually applied dynamically to schedule the job in the dynamic job-shop. The paper presents an adaptive iterative scheduling algorithm that operates dynamically to schedule the job in the dynamic job-shop. In order to get adaptive behavior, the reinforcement learning system is done with the phased Q-learning by defining the intermediate state pattern. We convert the scheduling problem into reinforcement learning problems by constructing a multi-phase dynamic programming process, including the definition of state representation, actions and the reward function. We use five heuristic rules, CNP-CR, CNP-FCFS, CNP-EFT, CNP-EDD and CNP-SPT, as actions and the scheduling objective: minimization of maximum completion time. So a complex dynamic scheduling problem can be divided into a sequential sub-problem easier to solve. We also analyze the time and the solution and present some experimental results. (CNP), State Pattern, Dynamic Scheduling.
Language英语
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/19982
Collection智能产线与系统研究室
Corresponding AuthorWei YZ(魏英姿)
Affiliation1.Shenyang Ligong University, Shenyang 110168, China
2.Shenyang Institute of Automation, Chinese Academy of Science, Shenyang 110016, China
Recommended Citation
GB/T 7714
Wei YZ,Jiang, Xinli,Hao, Pingbo,et al. Pattern driven dynamic scheduling approach using reinforcement learning[C]. NEW YORK:IEEE,2009:514-519.
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