Reconfigurable manufacturing system scheduling: a deep reinforcement learning approach

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dc.contributor.authorTang, Jiecheng
dc.contributor.authorHaddad, Yousef
dc.contributor.authorSalonitis, Konstantinos
dc.date.accessioned2022-05-30T15:46:03Z
dc.date.available2022-05-30T15:46:03Z
dc.date.issued2022-05-26
dc.description.abstractReconfigurable Manufacturing Systems (RMS) bring new possibilities toward meeting demand fluctuations while, at the same time, challenges scheduling efficiency. This paper presents a novel approach that, for the scheduling problem of RMS on multiple products, finds a dynamic control policy via a group of deep reinforcement learning agents. These teamed agents, embedded with a shared value decomposition network, aim on minimising the make-span of a constant updating order group by guiding a group of automated guided vehicles to move modules of machine, raw materials, and finished products inside the system.en_UK
dc.identifier.citationTang J, Haddad Y, Salonitis K. (2022) Reconfigurable manufacturing system scheduling: a deep reinforcement learning approach. Procedia CIRP, Volume 107, pp. 1198-1203. 55th CIRP Conference on Manufacturing Systems 2022, 29 June - 1 July 2022, Lugano, Switzerlanden_UK
dc.identifier.issn2212-8271
dc.identifier.urihttps://doi.org/10.1016/j.procir.2022.05.131
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17973
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectReconfigurable Manufacturing Systemen_UK
dc.subjectMulti-agent Systemen_UK
dc.subjectDeep Reinforcement Learningen_UK
dc.subjectFlexible Job-shop Scheduling Problemen_UK
dc.titleReconfigurable manufacturing system scheduling: a deep reinforcement learning approachen_UK
dc.typeConference paperen_UK

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