Tang, JiechengHaddad, YousefSalonitis, Konstantinos2022-05-302022-05-302022-05-26Tang 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, Switzerland2212-8271https://doi.org/10.1016/j.procir.2022.05.131https://dspace.lib.cranfield.ac.uk/handle/1826/17973Reconfigurable 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.enAttribution-NonCommercial-NoDerivatives 4.0 InternationalReconfigurable Manufacturing SystemMulti-agent SystemDeep Reinforcement LearningFlexible Job-shop Scheduling ProblemConference paper