Generative AI-powered planning: A hybrid graph-diffusion approach for demand-driven flexible manufacturing systems

Abstract

Flexible Smart Manufacturing Systems (FSMS) are critical to achieving mass customization and operational agility under Industry 4.0. However, planning effective FSMS configurations remains challenging due to fluctuating market demands, heterogeneous system components, complex interdependencies, and the need to optimize resource utilization. Conventional planning methods often require predefined line configurations and lack adaptability, scalability, and awareness of dynamic system properties. This paper presents a novel Hybrid Graph-Diffusion Based Planning Framework that integrates generative AI with system-theoretic modeling to autonomously generate optimal FSMS configurations based on different market demands. Specifically, we introduce a system model-embedded Heterogeneous Graph (HG) to represent the structure and properties of an FSMS and infuse it within a system property-tailored diffusion model to generate reconfigurable plan configurations. The final system property-guided refinement guarantees that the final plan configuration is optimal in both demand satisfaction and resource use. Furthermore, our ablation studies validate that our framework significantly outperforms conventional approaches in both demand satisfaction and resource efficiency. Furthermore, our ablation studies validate the effectiveness of the system property guidance and HG-based representation in enhancing planning feasibility, robustness, and adaptability.