GRU-based real-time scheduling method for production-logistics collaboration in digital twin workshop

Abstract

In modern workshops with high customization requirements, production is typically conducted under a small-batch, multi-variety order mode. Under such conditions, random order arrivals and fuzzy manufacturing times, caused by fluctuations in workshop conditions, present significant challenges to real-time scheduling and control. To address these issues, this study proposes a real-time scheduling method for production-logistics collaboration (RT-SMPLC) based on gated recurrent units (GRUs) in a digital twin (DT) workshop. Firstly, a comprehensive RT-SMPLC framework was constructed. Leveraging virtual-physical interaction, a dynamic mapping environment is established to capture the real-time status information of production elements. Secondly, the scheduling process is guided by a task priority index that facilitates the selection of the optimal production-logistics resource group for each task. This priority index is iteratively optimized through virtual evolution and GRU-based prediction. Finally, the operation assignment result is fed back to the physical workshop for execution in real time via industrial communication protocols and networks, enabling closed-loop control through virtual-to-physical interaction. The proposed method was validated on a DT-based experimental platform using real production cases. Comparative experiments across three different-scale scenarios and three algorithms demonstrate that RT-SMPLC effectively reduces makespan, energy consumption, and tardiness, while exhibiting robust real-time responsiveness.