Intelligent Scheduling for Permutation Flow Shop with Dynamic Job Arrival via Deep Reinforcement Learning

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	Intelligent Scheduling for Permutation Flow Shop with Dynamic Job Arrival via Deep Reinforcement Learning
	Yang SL(杨圣落)1,2,3; Xu ZG(徐志刚)2,3
	2021
会议日期	March 12-14, 2021
会议地点	Chongqing, China
关键词	dynamic scheduling deep reinforcement learning PFSP deep Q network (DQN) intelligent scheduling dynamic job arrival
页码	2672-2677
英文摘要	The dynamic permutation flow shop scheduling problem (PFSP) is receiving increasing attention in recent years. To provide intelligent scheduling for the dynamic PFSP, we solved the dynamic PFSP with new job arrival using deep reinforcement learning (DRL). The mathematical model is established with the objective of minimizing the total tardiness cost of all jobs arriving at the system. The double deep Q network (DDQN) is adapted to solve the studied problem. A large range of instances is provided to train the DDQN-based scheduling agent. The training curve shows the DDQN-based scheduling agent learned to choose appropriate actions at rescheduling points during the training process. After training, the trained model is saved and used to compare with several well-known dispatching rules on a set of test instances. The comparison results show that our trained scheduling agent performs significantly better than these dispatching rules. Our work can provide intelligent decision-making of scheduling for a flow shop under a dynamic production environment.
源文献作者	Chengdu Union Institute of Science and Technology ; Chongqing Geeks Education Technology Co., Ltd ; Chongqing Global Union Academy of Science and Technology ; Global Union Academy of Science and Technology ; IEEE Beijing Section
产权排序	1
会议录	2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISSN号	2689-6621
ISBN号	978-1-7281-8028-1
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/28745]
专题	沈阳自动化研究所_装备制造技术研究室
通讯作者	Yang SL(杨圣落)
作者单位	1.University of Chinese Academy of Sciences, Beijing, China 2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China 3.Chinese Academy of Sciences, Shenyang Institute of Automation, Shenyang, China
推荐引用方式 GB/T 7714	Yang SL,Xu ZG. Intelligent Scheduling for Permutation Flow Shop with Dynamic Job Arrival via Deep Reinforcement Learning[C]. 见:. Chongqing, China. March 12-14, 2021.