Generative AI-Driven Optimization in Flexible and Reconfigurable Manufacturing Systems

Salah Hammedi; Hicham Chaoui; Lotfi Nabli

doi:10.48084/etasr.16077

Authors

Salah Hammedi Networked Objects, Control, and Communication Systems (NOCCS), ENISo, University of Sousse, Tunisia | Electrical Engineering Department, National School of Engineers of Monastir, Monastir University, Tunisia
Hicham Chaoui Department of Electronics, Carleton University, Ottawa, Canada | Electrical and Computer Engineering Department, Old Dominion University, Norfolk, USA
Lotfi Nabli Networked Objects, Control, and Communication Systems (NOCCS), ENISo, University of Sousse, Tunisia | Electrical Engineering Department, National School of Engineers of Monastir, Monastir University, Tunisia

Volume: 16 | Issue: 3 | Pages: 36456-36469 | June 2026 | https://doi.org/10.48084/etasr.16077

Received: 6 November 2025 | Revised: 22 December 2025 | Accepted: 3 January 2026 | Online: 6 June 2026

Corresponding author: Hicham Chaoui

Abstract

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.

Keywords:

Flexible and Reconfigurable Manufacturing Systems (FRMSs), generative AI, Petri nets, intelligent scheduling, performance optimization, Industry 4.0

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