## 5 Conclusions  
An advanced platform for detailed 3D visualization was crafted. This platform has opened the door for real-time monitoring and process simulations, fostering transparency and comprehensibility. A noteworthy outcome of this study is creating a comprehensive platform that merges real-time monitoring, process simulation, and production scheduling into a singular digital twin system. This blend enables real-time operational state monitoring of the physical layer, predictive optimization, and system alerts, setting a new standard in solid-wood-panel processing. The value of this work rests on three main pillars. Firstly, constructing an intuitive 3D simulation interface provides a robust process visualization and management tool. Secondly, real-time mapping accurately mirrors physical production processes, providing a platform for swift problem detection and resolution. Lastly, introducing the DSAGA methodology within this virtual environment boosts the system's operational efficiency, far exceeding the performance of traditional algorithms. The development of this advanced 3D visualization platform signifies progress in solid-wood-panel processing by integrating real-time monitoring and predictive optimization. The flexibility of this system indicates potential applications not only in various production systems but also in the logistics and healthcare sectors, among others. This step forward exemplifies the impact of intelligent manufacturing practices across different industries. Expanding the digital twin framework beyond wood manufacturing to include diverse materials and processes sets the stage for widespread innovation in manufacturing practices. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **GA** \\ \hline \(P/\%\) & 8.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Percentage deviation for maximum completion time. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Maximum Completion Time** \\ \hline DSAGA & 5403 s \\ GA & 5933 s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different methods. To harness the full potential of digital twin technology within the manufacturing domain, it is necessary to extend research in several key areas. Firstly, future studies should scrutinize how adapting digital twin technology for various scales of wood processing enterprises can optimize production. Such an investigation is to validate technology scalability and appropriateness across disparate business models within the wood industry. Secondly, incorporating advanced technologies such as artificial intelligence (AI) and machine learning (ML) could substantially enhance the predictive analytics and optimization capabilities of the digital twin framework. By converging AI and ML with digital twin technology, manufacturing can benefit from process efficiency and quality control, driving competitive edges. Lastly, a rigorous assessment of the long-term effects of digital twin technology's implementation is warranted to offer insights into business performance, cost efficiency, and environmental sustainability. Investigating these impacts will provide a holistic understanding, enabling industries to utilize digital twin technology to its ultimate effectiveness. Drawing from the substantial promise shown by digital twin technology in the current study, these proposed research initiatives are expected to propel industry-wide adoption and innovation. Future exploration in these directions will not only contribute to the knowledge in smart manufacturing but will also spur the development of increasingly intelligent production systems fitting for the evolving manufacturing landscape. Conceptualization, J.Y. ; methodology, J.Y. ; software, J.Y., Y.W. and J.H; validation, J.Y. ; formal analysis, J.Y. ; investigation, L.T. ; resources, Y.Z. and J.W. ; data curation, Y.W., J.H. and L.T. ; writing--original draft preparation, J.Y. ; writing--review and editing, J.W. ; visualization, J.W. ; supervision, Y.Z. and J.W. ; project administration, J.W.