Generative AI-Driven Optimization in Flexible and Reconfigurable Manufacturing Systems
Résumé
Flexible and Reconfigurable Manufacturing Systems (FRMSs) are essential for coping with variability in modern production environments; however, efficient scheduling and rapid reconfiguration remain challenging. This paper presents a hybrid optimization framework that integrates Colored Petri Net (CPN) modeling with Generative Artificial Intelligence (GenAI) to enhance scheduling performance and system adaptability. The CPN formalism ensures verifiable modeling of system dynamics, while a transformer-based generative model produces candidate scheduling and reconfiguration strategies. Simulation experiments were conducted under static, dynamic, and adaptive scenarios, including machine breakdowns and dynamic job arrivals. Performance was evaluated using makespan, mean flow time, machine utilization, and reconfiguration latency. The results indicate that the proposed approach reduces the makespan by approximately 11–12% and improves machine utilization by 7–9% compared to classical heuristics and genetic algorithms, while in dynamic and adaptive scenarios, reconfiguration latency is reduced by up to 26%. These findings demonstrate that combining formal Petri net models with GenAI provides an effective mathematical framework for adaptive optimization in FRMSs.
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