Resilience-driven maintenance scheduling methodology for multi-agent production line system
Wang X(王潇); Qi C(祁超); Wang HW(王洪伟); Si QM(佀庆民); Zhang GW(张国伟)
2015
会议名称27th Chinese Control and Decision Conference, CCDC 2015
会议日期May 23-25, 2015
会议地点Qingdao, China
关键词resilience deteriorating quality states semi-Markov decision processes resource constraints multi-agent reinforcement learning
页码614-619
中文摘要In recent years, the public has been paying ever greater attention to problems related to resilience, since resilience presents the recovery ability to increasing complexity and vulnerability of disturbances. Many researches have been performed to address resilience of networks disruptions in manufacturing. However, a systematic method to model and analyze maintenance scheduling in disturbed production line system is not well developed. This paper considers a resilience-driven maintenance scheduling methodology under maintenance resource constraints for a simplified production line system, which consists of an upstream production machine, a downstream production machine and an intermediate buffer. The machines with degradation quality states represented by multiple decreasing yield levels are modeled as semi-Markov decision processes. A hierarchical and policy-coupled methodology based on reinforcement learning is used to determine maintenance policy of the system. The numerical results show that the application of the methodology to the aforementioned system can minimize the total cost and converge to the approximate optimal solution.
收录类别EI ; CPCI(ISTP)
产权排序3
会议录Proceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015
会议录出版者IEEE
会议录出版地Piscataway, NJ, USA
语种英语
ISBN号978-1-4799-7016-2
WOS记录号WOS:000375232901036
内容类型会议论文
源URL[http://ir.sia.cn/handle/173321/17196]  
专题沈阳自动化研究所_机器人学研究室
推荐引用方式
GB/T 7714
Wang X,Qi C,Wang HW,et al. Resilience-driven maintenance scheduling methodology for multi-agent production line system[C]. 见:27th Chinese Control and Decision Conference, CCDC 2015. Qingdao, China. May 23-25, 2015.
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