Intelligent Scheduling in Reconfigurable Manufacturing Systems Using Petri Nets and Hybrid Optimization
DOI:
https://doi.org/10.64229/3cv5jw94Keywords:
Petri Nets, Reconfigurable manufacturing systems, Scheduling optimization, Metaheuristics, Intelligent manufacturingAbstract
This paper proposes an intelligent scheduling methodology for Reconfigurable Manufacturing Systems (RMS) that integrates Petri Net (PN) modeling with heuristic and metaheuristic optimization techniques. The framework is validated through an industrial case study in automotive component manufacturing characterized by fluctuating demand, variable order sizes, and stochastic machine downtimes. Quantitative results show that the proposed approach reduces average production delays by 20%, increases resource utilization from 75% to 90%, and improves responsiveness to urgent orders by 25% compared with traditional scheduling methods. A comparative analysis further demonstrates that while rule-based scheduling and PN-based heuristics provide limited improvements under high variability, the integration of Genetic Algorithms (GA) and Ant Colony Optimization (ACO) ensures superior scalability, maintaining low tardiness even when the number of jobs increases significantly. By coupling a formal PN representation with adaptive decision mechanisms, the proposed methodology achieves both efficient scheduling and practical industrial applicability, addressing limitations of existing approaches that treat modeling and optimization separately.
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Copyright (c) 2026 Salah Hammedi (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
