Multimode approach using reinforcement learning and digital twin for operating mode management

Managing operating modes in a multimode production system represents a complex challenge that necessitates both meticulous and reactive planning. The challenge resides in coordinating and opti-mizing the different modes to address variations in demand while ensuring optimum makespan. A multimode system integrates multiple operating modes to accommodate any disturbances that may affect the system. This paper addresses the issue of selecting the appropriate mode to activate in re-sponse to the occurrence of a failure. By using Reinforcement Learning (RL) and Digital Twin (DT), the RL agent uses a state space (St) provided by a Digital Twin, to target its action (A) which consists of making a decision about which mode should be activated and which modes should be deactivated. The combination of the RL agent with the multimode system via the Digital Twin enables real-time adaptation to a dynamic environment, with the possibility of virtually testing the decisions made by the RL agent before their actual implementation, and consequently enhancing the performance of com-plex industrial systems. An innovative multimode scheduling approach will be targeted for the discrete case.

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Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).