Reinforcement Learning for Real-Time Scheduling in Dynamic Reconfigurable Manufacturing Systems

This study presents a novel application of Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) for scheduling optimization in Reconfigurable Manufacturing Systems (RMFS). The performance of these approaches is quantitatively evaluated and compared with traditional scheduling methods, specifically Shortest Processing Time (SPT) and Earliest Due Date (EDD), across several key metrics, including makespan, tardiness, resource utilization, and adaptability to disturbances. Our results show a significant reduction in makespan, with RL achieving a 20% improvement and DRL a 28.57% improvement over SPT. Moreover, RL and DRL outperform classical methods in minimizing tardiness and improving resource utilization. DRL also demonstrates superior adaptability under dynamic disruptions such as machine breakdowns, with only a 5% deviation in makespan compared to 16.67% for SPT. These findings confirm the benefits of RL and DRL for real-time decision-making in dynamic manufacturing environments. The study discusses the robustness and scalability of RL and DRL approaches, as well as the challenges related to their computational cost. The novelty lies in integrating RL and DRL into RMFS scheduling to offer a scalable, adaptive solution that improves production efficiency.

@article{Hammedi2025,
  title     = {Reinforcement Learning for Real-Time Scheduling in Dynamic Reconfigurable Manufacturing Systems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {6},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Salah Hammedi and Abdallah Namoun and Mohamed Shili},
  doi       = {10.14569/IJACSA.2025.0160639},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160639}
}