## 3 Results and Discussion
### Validation of Approach with Disruptions and Rescheduling  
To test the approach in a running factory environment, the algorithm selector made predictions on a dataset created from real-world data from the learning factory \"Werk 150\" at Reutlingen University. The learning factory represents a scooter production that uses AGVs to transport goods between different stations. \"Werk 150\" is a production company that replicates processes in the areas of product and work system engineering, incoming goods, storage, order picking, production, assembly, and distribution, which are fundamental for the verification of a scheduling system for AGVs [68]. All necessary processes that impact AGV scheduling are present. For delivering parts, there are three  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Machine Learning Algorithm with** & & & & \\
**Five-Fold Cross-Validation** & & & & \\ \hline Decision Tree & 0.7510 & 0.6934 & 0.5970 & 0.5640 \\ Random Forest & 0.7263 & 0.7181 & 0.5937 & 0.5378 \\ Logistic Regression & 0.8094 & 0.7266 & 0.6600 & 0.6102 \\ KNN & 0.7309 & 0.6315 & 0.6404 & 0.5840 \\ Neural network (Skleam) & 0.7843 & 0.6527 & 0.6338 & 0.6001 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Five-fold cross-validation of mean validation accuracy results.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Algorithm Accuracy Scores** & **JSSP** & **FJSSP** \\ \hline Decision Tree & 0.7848 & 0.6406 \\ Random Forest & 0.7089 & 0.9531 \\ Logistic Regression & 0.3924 & 0.8750 \\ KNN & 0.3797 & 0.8281 \\ Neural network (Skleam) & 0.6709 & 0.8750 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Accuracy of classification models for validation datasets.  
different transport vehicles available (Figure 7). Two AGVs and one AMR, which travel with an average velocity of 0.9 m/s. The transport orders of the vehicles vary among the production, depending on the outstanding work step. The transport orders are in JSON format and include information about the operation, the machine ID, the area where the transport takes place, the line, and the planned quantity. This file format complies with VDA5050, a standardization approach in the automotive industry in Germany, which shall ensure conflict-free and transparent interfaces between AGV fleets and the central executing software system, independent of the manufacturer.  
To test the functionality of the algorithm selector, the experiment included different scheduling scenarios. One scenario considered AGVs that were already assigned to tasks, whereas the second scenario offered alternatives for assigning the AGVs to orders. In this case, the scheduler allocated the eligible AGVs to individual tasks to minimize the makespan of the entire scenario. Furthermore, disruptions within the system were included to test the reaction of the approach to disturbances. The entering disruption included a timestamp that specified the already executed orders. These were deleted in the original scheduling problem in order to reschedule only the pending orders. In the tested scenario, an AGV failure appeared, and the system replaced the defective AGV with the corresponding substitute. After the adaptation, a new schedule was generated, and the execution of the plan continued, considering the downtime of the respective AGV.