Tang, JiechengHaddad, YousefPatsavellas, JohnSalonitis, Konstantinos2024-01-042024-01-042023-11-22Tang 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. 11082-110872405-8963https://doi.org/10.1016/j.ifacol.2023.10.814https://dspace.lib.cranfield.ac.uk/handle/1826/2060722nd IFAC World Congress, 9-14 July 2023, Yokohama, JapanRapid 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.enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Manufacturing plant controlReconfigurable manufacturing systemreinforcement learningschedulingproximal policy optimisationConference paper