A deep reinforcement learning based scheduling policy for reconfigurable manufacturing systems

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dc.contributor.authorTang, Jiecheng
dc.contributor.authorSalonitis, Konstantinos
dc.date.accessioned2021-10-25T10:52:26Z
dc.date.available2021-10-25T10:52:26Z
dc.date.issued2021-10-20
dc.description.abstractReconfigurable manufacturing systems (RMS) is one of the trending paradigms toward a digitalised factory. With its rapid reconfiguring capability, finding a far-sighted scheduling policy is challenging. Reinforcement learning is well-equipped for finding highly efficient production plans that would bring near-optimal future rewards. For minimising reconfiguring actions, this paper uses a deep reinforcement learning agent to make autonomous decision with a built-in discrete event simulation model of a generic RMS. Aiming at the completion of the assigned order lists while minimising the reconfiguration actions, the agent outperforms the conventional first-in-first-out dispatching rule after self-learning.en_UK
dc.identifier.citationTang J, Salonitis K. (2021) A deep reinforcement learning based scheduling policy for reconfigurable manufacturing systems. Procedia CIRP, Volume 103, pp. 1-7. 9th CIRP global Web conference (CIRPe 2021): Sustainable, resilient, and agile manufacturing and service operations : Lessons from COVID-19, 26-28 October 2021, Saint-Etienne, Franceen_UK
dc.identifier.issn2212-8271
dc.identifier.urihttps://doi.org/10.1016/j.procir.2021.09.089
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17197
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.subjectschedulingen_UK
dc.subjectreinforcement learningen_UK
dc.subjectdueling double deep q learningen_UK
dc.subjectdiscrete event simulationen_UK
dc.titleA deep reinforcement learning based scheduling policy for reconfigurable manufacturing systemsen_UK
dc.typeConference paperen_UK

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