Combining Petri Nets and AI Techniques to Improve Dynamic Production Scheduling Optimization

Salah Hammedi, Salah Hammedi, Haythem Chniti

2025

Abstract

This paper introduces an intelligent scheduling approach that integrates Petri nets and AI techniques to optimize real-time production in reconfigurable manufacturing systems (RMS) under uncertainty. Addressing key challenges such as resource allocation, downtime reduction, and dynamic adaptability, our method achieves an 85% success rate. By leveraging historical data, machine learning, and expert systems, it enhances throughput and minimizes idle time. Comparative analysis demonstrates that our approach outperforms existing static and dynamic methods, offering continuous adaptability to evolving conditions and superior resource allocation. These advancements establish a scalable framework for efficient and agile scheduling, setting a new standard for dynamic manufacturing environments.

Paper Citation

in Harvard Style

Hammedi S. and Chniti H. (2025). Combining Petri Nets and AI Techniques to Improve Dynamic Production Scheduling Optimization. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 1077-1084. DOI: 10.5220/0013261700003890

in Bibtex Style

@conference{icaart25,
author={Salah Hammedi and Haythem Chniti},
title={Combining Petri Nets and AI Techniques to Improve Dynamic Production Scheduling Optimization},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={1077-1084},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013261700003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Combining Petri Nets and AI Techniques to Improve Dynamic Production Scheduling Optimization
SN - 978-989-758-737-5
AU - Hammedi S.
AU - Chniti H.
PY - 2025
SP - 1077
EP - 1084
DO - 10.5220/0013261700003890
PB - SciTePress