Petri Net-Based Heuristic Scheduling for Improving the Efficiency of Reconfigurable Manufacturing Systems

Abstract

: This paper presents a hybrid scheduling framework for Reconfigurable Production Systems (RPS) that integrates Timed Petri Nets (TPNs) with three heuristic optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). TPNs are employed to formally model system dynamics, resource constraints, and task dependencies, while heuristic algorithms optimize scheduling decisions to minimize makespan, reduce tardiness, and improve resource utilization. By combining formal system modeling with adaptive optimization, the proposed approach ensures both schedule feasibility and operational efficiency in dynamic manufacturing environments. Experimental results demonstrate that the framework outperforms traditional scheduling rules, including FCFS, SPT, and EDD, achieving a 25 – 30% reduction in makespan and more than 20% improvement in resource utilization. Furthermore, the integration of Petri-net state feedback with heuristic search enhances adaptability and robustness under reconfiguration events. The proposed framework provides an effective decision-support solution for intelligent production scheduling and contributes to the development of flexible and efficient Industry 4.0 manufacturing systems.