Multi-objective reconfigurable manufacturing system scheduling optimisation: a deep reinforcement learning approach

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dc.contributor.authorTang, Jiecheng
dc.contributor.authorHaddad, Yousef
dc.contributor.authorPatsavellas, John
dc.contributor.authorSalonitis, Konstantinos
dc.date.accessioned2024-01-04T15:07:45Z
dc.date.available2024-01-04T15:07:45Z
dc.date.issued2023-11-22
dc.description22nd IFAC World Congress, 9-14 July 2023, Yokohama, Japan
dc.description.abstractRapid product design updates, unstable supply chains, and erratic demand phenomena are challenging current production modes. Reconfigurable manufacturing systems (RMS) aim to provide a cost-effective solution for responding to these challenges. However, given their complex adjustable nature, RMSs cannot fully unlock their potential by applying old-fashion fixed dispatching rules. Reinforcement learning (RL) algorithms offer a useful approach for finding optimal solutions in such complex systems. This paper presents a framework to train a scheduling agent based on a proximal policy optimisation (PPO) algorithm. The results of a numerical case study that implemented the framework on a simplified RMS model, suggest a good level of robustness and reveal areas of unpredictable behaviour that could be the focus of further research.en_UK
dc.identifier.citationTang J, Haddad Y, Patsavellas J, Salonitis K. (2023) Multi-objective reconfigurable manufacturing system scheduling optimisation: a deep reinforcement learning approach. IFAC-PapersOnLine, Volume 56, Issue 2, pp. 11082-11087en_UK
dc.identifier.issn2405-8963
dc.identifier.urihttps://doi.org/10.1016/j.ifacol.2023.10.814
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20607
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.subjectManufacturing plant controlen_UK
dc.subjectReconfigurable manufacturing systemen_UK
dc.subjectreinforcement learningen_UK
dc.subjectschedulingen_UK
dc.subjectproximal policy optimisationen_UK
dc.titleMulti-objective reconfigurable manufacturing system scheduling optimisation: a deep reinforcement learning approachen_UK
dc.typeConference paperen_UK
dcterms.dateAccepted2022-06-12

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