Characterizations and optimization for resilient manufacturing systems with considerations of process uncertainties

Abstract The recent COVID-19 pandemic reveals the vulnerability of global supply chains: the unforeseen supply crunches and unpredictable variability in customer demands lead to catastrophic disruption to the production planning and management, causing wild swings in productivity for most manufacturing systems. Therefore, a smart and resilient manufacturing system (S&RMS) is promising to withstand such unexpected perturbations and adjust promptly to mitigate their impacts on system stability. However, modeling the system&s resilience to the impacts of disruptive events has not been fully addressed. We investigate a generalized polynomial chaos expansion (gPCE) based discrete event dynamic system (DEDS) model to capture uncertainties and irregularly disruptive events for manufacturing systems. The analytic approach allows a real-time optimization for production planning to mitigate the impacts of intermittent disruptive events (e.g., supply shortages) and enhance the system&s resilience. The case study on a hybrid bearing manufacturing workshop suggests that the proposed approach allows a timely intervention in production planning to significantly reduce the downtime (around one-fifth of the downtime compared to the one without controls) while guaranteeing maximum productivity under the system perturbations and uncertainties.