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A reinforcement learning method for multi-AGV scheduling in manufacturing
Xue TF(薛天放); Zeng P(曾鹏); Yu HB(于海斌)
作者部门工业控制网络与系统研究室
会议名称19th IEEE International Conference on Industrial Technology, ICIT 2018
会议日期February 19-22, 2018
会议地点Lyon, France
会议主办者IEEE Industrial Electronics Society (IES) ; The Institute of Electrical and Electronics Engineers (IEEE)
会议录名称Proceedings of the IEEE International Conference on Industrial Technology
出版者IEEE
出版地New York
2018
页码1557-1561
收录类别EI
EI收录号20182105212521
产权排序1
ISBN号978-1-5090-5949-2
关键词Multi-agv Flow-shop Reinforcement Learning Markov Problem Optimal Solution
摘要

This paper addresses a multi-AGV flow-shop scheduling problem with a reinforcement learning method. Each AGV equipped with a robotic manipulator, operates on the fixed tracks, transporting semi-finished products between successive machines. The objectives dealt with here is to obtain a AGV schedule that minimize the average job delay and total makespan. After formulating such schedule problem as a Markov problem by defining state features, actions space and reward function, a new scheduling method is proposed, based on reinforcement learning. In this new method AGVs share full information on each machine's instant state and job being executed, making decisions thorough understanding of the entire flow shop. Simulation results demonstrate that this new method learns optimal or near-optimal solution from the past experience and provides better performance than multi-agent scheduling method in a dynamic environment.

语种英语
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/22064
专题工业控制网络与系统研究室
通讯作者Xue TF(薛天放)
作者单位Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
推荐引用方式
GB/T 7714
Xue TF,Zeng P,Yu HB. A reinforcement learning method for multi-AGV scheduling in manufacturing[C]//IEEE Industrial Electronics Society (IES), The Institute of Electrical and Electronics Engineers (IEEE). New York:IEEE,2018:1557-1561.
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