Real-Time Data-Driven Hybrid Synchronization for Integrated Planning, Scheduling, and Execution Toward Industry 5.0 Human-Centric Manufacturing

Flexible and reconfigurable manufacturing systems with independent multiskilled cells are pivotal for mass customization under volatile demand, yet internal/external uncertainties persistently disrupt planning, scheduling, and execution (PSE), causing idle time and workflow instability. This study proposes a hybrid synchronization framework with hierarchical real-time data feedback to address heterogeneous demand-capacity synchronization (HDCS), reconciling demand volatility, system reconfigurability, and dynamic human capacity. The framework first adopts the ticket-enabled queuing mechanism of the graduation intelligent manufacturing system (GiMS) to enable seamless PSE integration, ensuring resilient operations. Next, it hybridizes a global optimization model with local adjustment mechanisms, utilizing multigranularity data to balance global optimality and local practicality under uncertainty. Finally, it implements a human-cyber-physical digitalization architecture to establish smart human-machine collaborative assembly. Validated through a case study, the method enhances cost-efficiency by 1.7%, punctuality by 29.5%, and resource utilization by 3.9% compared to conventional rescheduling strategies, demonstrating superior adaptability to uncertainties. The results validate its effectiveness in advancing human-centric smart manufacturing aligned with Industry 5.0 objectives, offering an integrated solution to HDCS challenges through synergistic coordination of hierarchical control, hybrid optimization, and human-machine collaboration.