Degradation-aware decision making in reconfigurable manufacturing systems

Reconfigurable manufacturing systems (RMS) are designed to improve responsiveness and adaptability to individualized demands, creating a potential solution for mass personalization. System reconfigurations provide flexibility to fluctuating demands, and can be enhanced by adjustments of machine components. However, improper balancing between maintenance and reconfiguration actions can result in system breakdowns and can hamper system health and ability to reconfigure. This paper proposes a degradation-aware RMS decision-making model to optimally determine and adjust operational actions in real-time considering demand fulfilment, maintenance cost, and system health. The proposed approach has the capability to capture the causality between operational action sequences and the resulting system deterioration through artificial intelligence-based methods.