# Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems
## 4 Case Study
### Results and Discussion  
Figure 12 shows the initial scheduling plan generated by the scheduling system based on the order data, without considering the future availability of the AGV. In the initial scheduling plan, 29 transportation tasks were completed by three AGVs in 1448 s, which met the demand that the execution system must complete all tasks within 25 min according to the order information. Figure 13 shows the AGV power curve predicted in the virtual model when the task is executed according to the initial schedule. It can be seen that the battery SOC of AGV3 will reach the charging threshold when the system time is close to 600 s. The AGV3 will perform the current transportation task and then go to charge. The charging process continues until the SOC reaches 100%. Figure 14 shows the evaluation results of the initial scheduling plan in Flexsim. The evaluation results show that makespan is 2385 s when the initial scheduling plan is executed in Flexsim. Due to the charging behaviour of AGV3, the delivery time of the task is seriously delayed and cannot be meeting the delivery requirements of this batch of orders. Furthermore, it caused a serious reduction in the utilization rate of the AGVs. Therefore, the initial scheduling plan must be adjusted. Figure 12: Initial scheduling plan. Figure 13: The predicted AGV SOC curve when the initial schedule is executed. Figure 15 shows the AGV Gantt chart under different strategies. Table 2 shows the total energy consumption of the system under different scheduling strategies. Therefore, the initial scheduling plan must be adjusted. For the scenarios described above, if the traditional dynamic scheduling method is adopted, the remaining tasks are usually redistributed when the battery of the AGV3 battery is warned. The traditional scheduling system will let the AGV3 charge immediately after the current transportation task is executed. The remaining tasks will be allocated to the other two AGVs, and the scheduling results obtained are shown in Figure 15a. As shown in Figure 15a, the traditional dynamic scheduling can respond quickly to dynamic events. It will update the scheduling plan in time when the battery of the AGV3 is insufficient and needs to be charged, which can effectively reduce the delayed \begin{table}
\begin{tabular}{c c} \hline \hline
**Schedule Plan** & **Total Energy Consumption** \\ \hline Keep the initial scheduling plan & 87.0522 \\ Traditional dynamic scheduling & 83.0553 \\ DTDAS & 81.9582 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Total energy consumption under different scheduling plans. Figure 14: Evaluation results of the initial scheduling plan in Flexsim. Figure 15: AGV Gantt chart under different strategies. (**a**) when traditional dynamic scheduling is adopted. (**b**) when DTDAS is adopted. time caused by a certain AGV charging. However, the scheduling makespan generated by the traditional dynamic scheduling system is 1648 s, which still cannot meet the delivery demand. That being said, the DTDAS proposed in this paper can predict the availability of AGV. Therefore, we can use this technology to solve the problems encountered in traditional dynamic scheduling. After the initial scheduling is generated and verified, it is sent to the execution system. The execution system starts to perform tasks and continuously feeds back data to the virtual system. At the same time, the virtual system will predict the availability of the AGV in the future. After that, it will decide whether to update the scheduling plan based on the predicted results. During the execution of the initial scheduling plan, the virtual system predicts the availability result of AGV3, and this prediction result shows that the transportation system will seriously exceed the delivery time to complete this batch of tasks. Therefore, the virtual system triggers the charging schedule in advance. In the charging schedule, the AGV3 is asked to perform a short charging behaviour before performing task 2-3. After that, AGV3 will continue to perform the transportation task.