Pattern driven dynamic scheduling approach using reinforcement learning
文献类型：会议论文
| 作者 | Wei YZ(魏英姿); Jiang, Xinli; Hao, Pingbo; Gu KF(谷侃锋)
|
| 出版日期 | 2009 |
| 会议名称 | 2009 IEEE International Conference on Automation and Logistics, ICAL 2009 |
| 会议日期 | August 5-7, 2009 |
| 会议地点 | Shenyang, China |
| 关键词 | Reinforcement Learning Contract Net Protocol (CNP) State Pattern Dynamic Scheduling |
| 页码 | 514-519 |
| 通讯作者 | 魏英姿 |
| 中文摘要 | Production 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. |
| 收录类别 | EI ; CPCI（ISTP） |
| 产权排序 | 2 |
| 会议录 | Proceedings of the 2009 IEEE International Conference on Automation and Logistics, ICAL 2009
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | NEW YORK |
| 语种 | 英语 |
| ISBN号 | 978-1-4244-4795-4 |
| WOS记录号 | WOS:000291503400097 |
| 源URL | [http://ir.sia.cn/handle/173321/19982]  |
| 专题 | 沈阳自动化研究所_装备制造技术研究室 |
| 推荐引用方式 GB/T 7714 | Wei YZ,Jiang, Xinli,Hao, Pingbo,et al. Pattern driven dynamic scheduling approach using reinforcement learning[C]. 见:2009 IEEE International Conference on Automation and Logistics, ICAL 2009. Shenyang, China. August 5-7, 2009. |
入库方式： OAI收割
来源：沈阳自动化研究所
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