## 3 Results and Discussion
### Machine Learning Training and Evaluation  
After implementing the selected machine learning models, they were trained on the designed datasets. Furthermore, feature engineering ensured the assessment of feature importance. Three different methods were applied for measuring feature importance: a heatmap with the Pearson correlations, the built-in class feature importance of tree-based classifiers, and random forest classifiers for evaluating feature importance. After selecting the features with the highest correlation to the output, the machine learning models were trained. The evaluation phase was ensured to test the robustness of the models and their validity. It implied the determination of the correct machine learning models to ensure the meeting of requirements and to solve the initial problem. For classification problems, the accuracy, recall, precision, and F1 score are key performance indicators that build on the principles of true positives, false negatives, true negatives, and false positives. The results of the JSSP dataset evaluation are presented in Tables 6 and 7. The tested models for classification problems provided successful implementations. Nevertheless, depending on the problem class, the performance altered. For the JSSP dataset with a solving time of 30 s, the neural network using the Keras implementation, decision trees, and KNN provided the best results, considering the tested performance metrics. For the 300 s solving time, random forest and KNN provided the best accuracy \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Machine Learning Algorithm** & **Solving Time** & **Parameter Tuning** & **Accuracy** & **Recall** & **Precision** & **F1 Score** \\ \hline \multirow{2}{*}{Decision Tree} & 30 s & Depth = 1 & 0.8361 & 0.8361 & 0.6990 & 0.7614 \\  & 300 s & Depth = 17 & 0.8033 & 0.8033 & 0.8487 & 0.8254 \\ \hline \multirow{2}{*}{Random Forest} & 30 s & Estimators = 100 & 0.8033 & 0.8033 & 0.8033 & 0.8033 \\  & 300 s & Estimators = 100 & 0.8525 & 0.8525 & 0.8580 & 0.8553 \\ \hline \multirow{2}{*}{Logistic Regression} & 30 s & Max\_iter = 1500 & 0.8197 & 0.8197 & 0.8580 & 0.8384 \\  & 300 s & Max\_iter = 1500 & 0.8197 & 0.8197 & 0.8898 & 0.8533 \\ \hline \multirow{2}{*}{KNN} & 30 s & Neighbors = 18 & 0.8525 & 0.8746 & 0.8525 & 0.8634 \\  & 300 s & Neighbors = 20 & 0.8525 & 0.8525 & 0.8750 & 0.8636 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance metrics of machine learning models on JSSP datasets. Figure 6: Makespan differences by algorithm selection per instance for FJSSP. scores. However, the close values of the performance metrics made a concrete decision for the best machine learning technique difficult. Therefore, the selection of the best machine learning model must consider the dataset used. Compared to the JSSP datasets, FJSSP achieved overall weaker results in terms of accuracy, precision, and recall. The highest scores for the 30 s solving time were obtained by the neural network with Sklearn and the decision tree, with an accuracy of 75%. For the 300 s solving time dataset, KNN and decision trees achieved the highest performance metrics. Consequently, there was not one machine learning algorithm providing the best results. The structure of the input data is an important criterion for future prediction accuracy. Therefore, the assessment of machine learning algorithm performance on the given data structure must be part of implementing the algorithm approach for an AGV scheduling use case.