Resilience-enhancing multi-strategy decision-making for dynamic scheduling in manufacturing systems

High-impact disruptions can cause significant performance degradation and even failures in manufacturing systems. Resilient manufacturing systems can absorb such disruptions, adapt to changing environments, and accelerate recovery through strategy scheduling based on real-time performance data. However, the nonlinear nature of degradation processes can lead to deviations from expected recovery outcomes and delays in strategy scheduling, which makes strategy scheduling for repairing manufacturing systems a difficult decision-making problem. Therefore, a resilience-enhancing multi-strategy decision-making for dynamic scheduling model in manufacturing systems is proposed, aiming to determine the optimal strategy and reduce performance anomaly duration. First, a component-based evaluation method is proposed to measure the absorption, adaptation, and recovery capabilities of the system, achieving real-time analysis of resilience levels. Then, a dynamic strategy scheduling method based on Markov chains is proposed to plan strategies and predict trajectories based on the real-time performance status, disruption, and resilience level, which solves the nonlinearity changes of performance state. Finally, a multi-strategy decision-making method based on fuzzy-BWM is proposed to achieve the resilient-oriented multi-objective discrete strategy decision-making, considering cost, recovery time, and recovery degree. The die forging press is used to demonstrate the effectiveness of the proposed model. The results show that the strategy decided by the model enables the system to recover quickly to its expected state with an acceptable cost compared to other strategies.