# Hybrid NHPSO-JTVAC-SVM Model to Predict Production Lead Time
## 4 Experimental Results  
We conducted prediction experiments using test data to verify the proposed NHPSO-JTVAC-SVM model. We compared the model with SVM, NHPSO-JTVAC-SVM, BA-SVM, GA-SVM, and GOA-SVM. The 5-fold CV scores in the iterative process of the integrated models are shown graphically in Figure 7a,b. The best 5-fold CV scores searched by the five models are listed in Table 5. The results demonstrated that the NHPSO-JTVAC algorithm had the best search performance with the best fitness values of 12.92% and 20.19% in the block assembly process performance dataset and pre-outfitting process performance dataset, respectively. Figure 7: Generation of the optimization models in (**a**) block assembly process performance dataset and (**b**) block pre-outfitting process performance dataset. Table 6 shows the optimal values of the three SVM parameters (\(C\), \(\varepsilon\), and \(\sigma\)) for each SVM-based model. In addition, Table 7 shows the test accuracy of these models based on the MAE, RMSE, and MAPE. The test error of MAPE, which we set as the fitness function of the optimization process, is shown graphically in Figure 8. We observed that the NHPSO-JTVAC-SVM model had the smallest MAPE in the training set (5-fold CV) and the smallest error in the test set. The results indicated that the test errors of the NHPSO-JTVAC-SVM model were the smallest in these models. In the block assembly process performance dataset, the MAPE of the NHPSO-JTVAC-SVM algorithm was 11.79%, and the MAE was 0.89. Moreover, in the pre-outfitting process performance dataset, the MAPE and MAE were 17.86% and 0.96, respectively. In addition, the NHPSO-JTVAC-SVM model was significantly better than the SVM model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Dataset** & \(C\) & \(\varepsilon\) & \(\sigma\) \\ \hline PSO-SVM & Assembly process performance data & 6.80 & 0.0172 & 6.8432 \\  & Pre-outfitting process performance data & 604.69 & 0.0157 & 0.0864 \\ \hline NHPSO-JTVAC-SVM & Assembly process performance data & 5.87 & 0.0010 & 7.6115 \\  & Pre-outfitting process performance data & 997.94 & 0.0192 & 0.0666 \\ \hline BA-SVM & Assembly process performance data & 113.65 & 0.1403 & 1.2904 \\  & Pre-outfitting process performance data & 201.55 & 0.3233 & 0.0509 \\ \hline GA-SVM & Assembly process performance data & 13.03 & 0.0353 & 4.7538 \\  & Pre-outfitting process performance data & 228.40 & 0.0283 & 0.1842 \\ \hline GOA-SVM & Assembly process performance data & 6.23 & 0.3650 & 6.3992 \\  & Pre-outfitting process performance data & 143.44 & 0.3556 & 0.0853 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Optimal values of the three SVM parameters (\(C\), \(\varepsilon\), and \(\sigma\)). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & & **SVM** & **PSO-SVM** & \begin{tabular}{c} **NHPSO-** \\ **JTVAC-SVM** \\ \end{tabular} & **BA-SVM** & **GA-SVM** & **GOA-SVM** \\ \hline Assembly process & \begin{tabular}{c} MAPE (\%) \\ MAE \\ RMSE \\ \end{tabular} & \begin{tabular}{c} 13.40 \\ 0.99 \\ 1.25 \\ 1.25 \\ \end{tabular} & \begin{tabular}{c} 11.81 \\ 0.89 \\ 1.23 \\ \end{tabular} & \begin{tabular}{c} **11.79** \\ 0.89 \\ 1.23 \\ \end{tabular} & \begin{tabular}{c} 12.55 \\ 0.95 \\ 1.29 \\ \end{tabular} & \begin{tabular}{c} 11.96 \\ 0.90 \\ 1.25 \\ \end{tabular} & \begin{tabular}{c} 12.14 \\ 0.90 \\ 1.09 \\ 1.09 \\ 1.08 \\ \end{tabular} \\ \hline Pre-outfitting process & \begin{tabular}{c} MAPE (\%) \\ MAE \\ RMSE \\ \end{tabular} & \begin{tabular}{c} 12.24 \\ 1.05 \\ 1.95 \\ \end{tabular} & \begin{tabular}{c} 17.98 \\ 0.96 \\ 1.91 \\ \end{tabular} & \begin{tabular}{c} **17.86** \\ 0.96 \\ 1.91 \\ \end{tabular} & \begin{tabular}{c} 29.99 \\ 0.97 \\ 2.19 \\ \end{tabular} & \
\begin{tabular}{c} 20.21 \\ 0.97 \\ **1.86** \\ \end{tabular} &
\begin{tabular}{c} 25.58 \\ 1.09 \\ 1.92 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Test errors of each model.