# Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems
## 5 Conclusions  
A digital twin-based dynamic AGV scheduling (DTDAS) method has been proposed in this paper. An overall framework of DTDAS was presented first. Then the knowledge support system of DTDAS was explored. A new mathematical model of AGV scheduling was finally established. Compared to the traditional dynamic scheduling method, the proposed DTDAS can not only obtain more accurate data to support the scheduling model, but also evaluate and visualize the AGV scheduling process. Meanwhile, the battery management was integrated into the mathematical model of AGV scheduling. Hence,  
Figure 16: The AGV SOC curve when DTDAS is adopted.  
the presented DTDAS can reasonably arrange the charging process to effectively improve the operating efficiency of the AGV transportation system in a workshop. A case study has been carried out and compared the traditional dynamic scheduling method, and it was found that the proposed method can reduce makespan and energy consumption by 10.7% and 1.32%, respectively. In general, this paper provides a new solution to the AGV scheduling problem in discrete manufacturing workshops. The application of digital twin mainly enhances the AGV scheduling technology in two aspects, providing knowledge support before the scheduling plan is generated, and comprehensive evaluation after the scheduling plan is generated. The great potential of DTDAS has been demonstrated by considering the charging problem of AGV in a manufacturing workshop. The DTDAS can theoretically be extended to any workshop with rechargeable AGVs to improve the efficiency of the transport system. However, the dynamic events considered in this paper are not comprehensive enough. In future work, the impact of other dynamic events like AGV failures or emergency tasks that occur on the scheduling system and their corresponding scheduling strategies will be studied.  
Conceptualization, J.X.; methodology, J.X.; software, W.H.; validation, W.H., Z.S., B.L., K.Z. and Z.Z.; formal analysis, W.H.; investigation, J.X.; resources, Z.S., K.Z. and Z.Z.; data curation, W.H., Z.S., B.L. and Z.Z.; writing--original draft preparation, W.H.; writing--review and editing, J.X. and Z.S.; visualization, Z.S., B.L. and K.Z.; supervision, J.X. and X.M.; project administration, X.M.; funding acquisition, J.X. and X.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (2020 YFB1708400).  
Not applicable.  
This work was supported by the National Key Research and Development Program of China (2020 YFB1708400).  
The authors declare no conflict of interest.  
Not applicable.  
This work was supported by the National Key Research and Development Program of China (2020 YFB1708400).  
The authors declare no conflict of interest.  
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Nomenclature**} \\ \hline \(MS\) & Makespan & \(tSO_{ij}\) & The time when \(O_{ij}\) began to be transported \\ \(E\) & The total energy consumed by all AGVs & \(dCO_{ij}\) & The distance from the loading point of \(O_{ij}\) to the unloading point of \(O_{ij}\) \\ \(O_{i}\) & Order \(i\) & \(tCO_{ij}\) & The time required arrive at the unloading point of \(O_{ij}\) \\ \(i\) & Index of orders, \(i=1,2,3,\dots\) & \(tWO_{ij}^{a}\) & The waiting time of \(A^{a}\) at the loading point of \(O_{ij}\) \\ \(n\) & Total number of orders & \(tSPO_{ij}\) & The time when the processing operation corresponding to the transportation task \(O_{ij}\) began to be processed \\ \(p_{i}\) & Total number of transportation operations for each order & \(tPO_{ij}\) & The time required for the processing operation corresponding to the transportation task \(O_{ij}\) \\ \hline \hline \end{tabular}