Optimizing Production in Reconfigurable Manufacturing Systems with Artificial Intelligence and Petri Nets

This article presents an advanced approach to optimize production in Reconfigurable Manufacturing Systems (RMFS) by integrating Petri Nets with artificial intelligence (AI) techniques, particularly a genetic algorithm (GA). The proposed methodology aims to enhance scheduling efficiency and adaptability in dynamic manufacturing environments. Quantitative analysis demonstrates significant improvements, with the approach achieving an 85% success rate in reducing lead times and improving resource utilization, outperforming traditional scheduling methods by a margin of 15%. Furthermore, our AI-driven system exhibits a 90% success rate in providing data-driven insights, leading to more informed decision-making processes compared to existing neural network optimization techniques. The scalability of the proposed method is evidenced by its consistent performance across various RMS configurations, achieving an 80% success rate in optimizing scheduling decisions. This study not only validates the robustness of the proposed method through extensive benchmarking but also highlights its potential for widespread adoption in real-world manufacturing scenarios. The findings contribute to the advancement of intelligent manufacturing by offering a novel, efficient, and adaptable solution for complex scheduling challenges in RMFS.

@article{Hammedi2024,
  title     = {Optimizing Production in Reconfigurable Manufacturing Systems with Artificial Intelligence and Petri Nets},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Salah Hammedi and Jalloul Elmelliani and Lotfi Nabli and Abdallah Namoun and Meshari Huwaytim Alanazi and Nasser Aljohani and Mohamed Shili and Sami Alshmrany},
  doi       = {10.14569/IJACSA.2024.0151044},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151044}
}