Optimization of Flexible Job Shop Scheduling With Machine Tool Breakdowns

The growing number of cyber-attacks on industrial systems increasingly affects manufacturing by causing unexpected machine tool failures and workflow interruptions. These security-driven disturbances demand scheduling models that can adapt rapidly. This research paper addresses the flexible job shop scheduling problem under temporary machine breakdowns triggered by cyber-attacks. After the attack is resolved, the affected machine tools must be efficiently reintegrated into the rescheduling process to maintain production stability. To generate effective rescheduling, this research applies the Genetic Algorithm, a biologically inspired metaheuristic especially suitable for solving complex NP-hard scheduling problems. The proposed method supports adaptive, real-time rescheduling while further addressing the optimization of two key performance criteria: balanced machine utilization and mean flow time. The approach is implemented in MATLAB® and validated through simulations on relevant benchmark problems. Experimental results confirm improved responsiveness, better resource balance, and enhanced efficiency under cyber-induced machine failures, contributing to a more flexible and resilient rescheduling.