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The integration of intelligent manufacturing within Industry 4.0 brings complex task scheduling challenges to modern production environments. To address this issue, we introduce an Adaptive Particle Swarm Optimization-Simulated Annealing (APSO-SA) hybrid algorithm. Our algorithm is specifically designed to solve the intricate task scheduling problems found in multi-stage workshops. The APSO-SA employs an innovative encoding method that broadens the solution space and improves the stability of the search process. It merges the global search power of Particle Swarm Optimization (PSO) with the local search accuracy of Simulated Annealing (SA), adeptly managing the trade-off between exploration and exploitation. Besides, we incorporate an adaptive parameter adjustment strategy which ensures the algorithm’s effectiveness across different scales of task sets. Experimental results confirm the superiority of our proposed APSO-SA algorithm over the existing algorithms, demonstrating its ability to swiftly converge upon optimal solutions. This suggests that the proposed APSO-SA algorithm holds the promise of substantially boosting production efficiency within the context of smart manufacturing systems.

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Adaptive Particle Swarm Optimization-Simulated Annealing for Complex Workshop Task Scheduling

  • 会议论文

  • 发表日期：

    2025年

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  • Xing Liu,
  • Yongheng Liu,
  • Juan Zhao,
  • Guangxia Xu

摘要

The integration of intelligent manufacturing within Industry 4.0 brings complex task scheduling challenges to modern production environments. To address this issue, we introduce an Adaptive Particle Swarm Optimization-Simulated Annealing (APSO-SA) hybrid algorithm. Our algorithm is specifically designed to solve the intricate task scheduling problems found in multi-stage workshops. The APSO-SA employs an innovative encoding method that broadens the solution space and improves the stability of the search process. It merges the global search power of Particle Swarm Optimization (PSO) with the local search accuracy of Simulated Annealing (SA), adeptly managing the trade-off between exploration and exploitation. Besides, we incorporate an adaptive parameter adjustment strategy which ensures the algorithm’s effectiveness across different scales of task sets. Experimental results confirm the superiority of our proposed APSO-SA algorithm over the existing algorithms, demonstrating its ability to swiftly converge upon optimal solutions. This suggests that the proposed APSO-SA algorithm holds the promise of substantially boosting production efficiency within the context of smart manufacturing systems.