Tang, JiechengSalonitis, Konstantinos2021-10-252021-10-252021-10-20Tang 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, France2212-8271https://doi.org/10.1016/j.procir.2021.09.089https://dspace.lib.cranfield.ac.uk/handle/1826/17197Reconfigurable 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.enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Reconfigurable manufacturing systemschedulingreinforcement learningdueling double deep q learningdiscrete event simulationConference paper