Time-based resilience metric for smart manufacturing systems and optimization method with dual-strategy recovery

Smart manufacturing (SM) enables production scheduling to automatically adjust the original plan to meet customer demands. The deep integration of advanced information technologies also makes SM systems prone to a wide range of possible attacks. However, rapid recovery in response to damage has not aroused enough recognition. This paper addresses the issue of resilience metrics and recovery optimization for SM systems. First, the characteristics of smart manufacturing scheduling (SMS), coupled with its execution, damage, and dual-strategy recovery behaviors, are analyzed. Afterward, a novel time-based resilience metric oriented toward the quantification of rapid recovery is proposed. Furthermore, a decision-making framework composed of a joint optimization model and a modified pigeon-inspired optimization algorithm with an enhanced learning strategy and crossover operator (PIOLC) is established. Last, a case study of a flexible job shop scheduling problem with 8 jobs and 8 machines is conducted to verify the effectiveness of the work. Experimental results show that the proposed approach can achieve dual-strategy recovery optimization by increasing the system resilience to 86.2%.