# Hybrid NHPSO-JTVAC-SVM Model to Predict Production Lead Time
## 4 Experimental Results
\begin{table}
\begin{tabular}{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} Kfold\_1 \\ Kfold\_2 \\ Kfold\_3 \\ Kfold\_4 \\ Kfold\_5 \\ Kfold\_6 \\ Kfold\_6 \\ Kfold\_7 \\ \end{tabular} & \begin{tabular}{c} 13.71\% \\ 13.96\% \\ 13.96\% \\ 13.94\% \\ 13.27\% \\ 13.17\% \\ 13.14\% \\ 13.82\% \\ 12.94\% \\ 13.82\% \\ 12.94\% \\ \end{tabular} & \begin{tabular}{c} 13.26\% \\ 12.44\% \\ 12.44\% \\ 13.45\% \\ 12.31\% \\ 13.45\% \\ 12.92\% \\ 13.61\% \\ \end{tabular} & \begin{tabular}{c} 13.24\% \\ 12.41\% \\ 13.25\% \\ 12.53\% \\ 13.30\% \\ 13.32\% \\ 13.06\% \\ \end{tabular} & \begin{tabular}{c} 14.01\% \\ 13.42\% \\ 13.68\% \\ 13.41\% \\ 13.45\% \\ 13.45\% \\ 13.45\% \\ 13.45\% \\ \end{tabular} & \begin{tabular}{c} 13.42\% \\ 12.61\% \\ 13.10\% \\ 13.42\% \\ 13.10\% \\ \end{tabular} & \begin{tabular}{c} 13.68\% \\ 13.42\% \\ 12.61\% \\ 13.41\% \\ 13.45\% \\ 13.06\% \\ \end{tabular} \\ \hline Pre-outfitting process & \begin{tabular}{c} Kfold\_1 \\ Kfold\_2 \\ Kfold\_3 \\ Kfold\_4 \\ Kfold\_5 \\ Kfold\_6 \\ Kfold\_7 \\ \end{tabular} & \begin{tabular}{c} 24.29\% \\ 20.98\% \\ 23.55\% \\ 20.28\% \\ 22.79\% \\ 23.53\% \\ \end{tabular} & \begin{tabular}{c} 20.60\% \\ 20.95\% \\ 20.95\% \\ 20.28\% \\ 20.11\% \\ \end{tabular} & \begin{tabular}{c} 20.49\% \\ 34.42\% \\ 35.04\% \\ 35.04\% \\ 35.04\% \\ 33.04\% \\ 33.61\% \\ \end{tabular} & \
\begin{tabular}{c} 34.42\% \\ 22.51\% \\ 23.17\% \\ 22.54\% \\ 22.54\% \\ 22.10\% \\ 22.66\% \\ 22.67\% \\ 22.67\% \\ \end{tabular} &
\begin{tabular}{c} 22.14 \\ 1.05 \\ 1.09 \\ 1.09 \\ 1.92 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: K-fold CV scores (MAPE) of each model. Table 8 lists the average MAPE values based on two datasets and obtained using SVM, PSO-SVM, NHPSO-ITVAC-SVM, BA-SVM, GA-SVM, and GOA-SVM. The average MAPE for the NHPSO-ITVAC-SVM model was 14.83%, which was the smallest among the AI models. Furthermore, Figure 9 shows the predicted results of the test set for different datasets, wherein the NHPSO-JTVAC-SVM model was superior in solving the lead-time-prediction problems. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **SVM** & **PSO-SVM** & \begin{tabular}{c} **NHPSO-ITVAC-SVM** \\ **SVM** \\ \end{tabular} & \begin{tabular}{c} **BA-SVM** \\ \end{tabular} & \
\begin{tabular}{c} **GA-SVM** \\ \end{tabular} &
\begin{tabular}{c} **GOA-SVM** \\ \end{tabular} \\ \hline MAPE (\%) & 17.32 & 14.90 & **14.83** & 21.27 & 16.09 & 18.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average MAPE of SVM, PSO-SVM, NHPSO-JTVAC-SVM, BA-SVM, GA-SVM, and GOA-SVM. Figure 8: Test MAPE of each model in (**a**) block assembly process performance dataset and (**b**) block pre-outfitting process performance dataset. Finally, we compared the proposed NHPSO-JTVAC-SVM model with other conventional ML models, such as the ElasticNet and adaptive boosting (AdaBoost) models. The results indicated that the NHPSO-JTVAC-SVM model we developed had the best performance (Table 9).