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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)
Department工业控制网络与系统研究室
Conference Name19th IEEE International Conference on Industrial Technology, ICIT 2018
Conference DateFebruary 19-22, 2018
Conference PlaceLyon, France
Author of SourceIEEE Industrial Electronics Society (IES) ; The Institute of Electrical and Electronics Engineers (IEEE)
Source PublicationProceedings of the IEEE International Conference on Industrial Technology
PublisherIEEE
Publication PlaceNew York
2018
Pages1557-1561
Indexed ByEI
EI Accession number20182105212521
Contribution Rank1
ISBN978-1-5090-5949-2
KeywordMulti-agv Flow-shop Reinforcement Learning Markov Problem Optimal Solution
Abstract

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.

Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/22064
Collection工业控制网络与系统研究室
Corresponding AuthorXue TF(薛天放)
AffiliationLab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Recommended Citation
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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