@article{li_yin_yang_chen_dong_chen_yang_2023, 
  title={Modeling of Digital Twin Workshop in Planning via a Graph Neural Network: The Case of an Ocean Engineering Manufacturing Intelligent Workshop}, 
  volume={13}, 
  DOI={10.3390/app131810134}, 
  abstractNote={<jats:p>In the era of Industry 4.0 to 5.0, the manufacturing industry is dedicated to improving its production efficiency, control capability and competitiveness with intelligent enhancement. As a typical discrete manufacturing industry, it is difficult for ocean engineering (OE) manufacturers to accurately control the entire production process, and the establishment of an integrated system supported by digital twin (DT) technology is a better solution. This paper proposes a comprehensive set of system architectures for the DT workshop. It focuses on planning, which is the main line of control, to establish a model based on graph neural networks (GNNs) and suggests five decision-support approaches associated with the model from a practical application perspective. The utilization of complete twin data for prediction and visual simulation effectively eliminates the problem of unexpected factors interfering with scheduling in enterprise production planning and achieves the goals of rapid processing and just-in-time completion. The planning model is based on the attention mechanism, which characterizes the disjunctive graph, extracts the input GNN, and outputs the scheduling decision by constructing the multi-attention network of operations and machines to deal with the complicated "operation–machine" combination relationship. The proposed method has been verified in the case of structural assembly and welding workshops, has validity and reliability, and is superior to the traditional priority scheduling rules and heuristics in terms of precision rate and rapidity. Furthermore, the DT system completes the production line application, and its proven reliability supports its full-scale application in future smart factories.</jats:p>}, 
  number={18}, 
  publisher={MDPI AG}, 
  author={Li, Jinghua and Yin, Wenhao and Yang, Boxin and Chen, Li and Dong, Ruipu and Chen, Yidong and Yang, Hanchen}, 
  year={2023}, 
  month={Sep}
  }