# Hybrid NHPSO-JTVAC-SVM Model to Predict Production Lead Time
## 5 Conclusions  
Based on the analysis of the parameter performance of SVMs, this paper proposes a hybrid NHPSO-JTVAC-SVM lead-time-prediction model. It fully utilizes the global search feature of the NHPSO-JTVAC algorithm to optimize the parameters of an SVM, which overcomes the blindness of SVM parameter selection. Compared with commonly  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & \multicolumn{2}{c}{**ElasticNet**} & \multicolumn{2}{c}{**AdaBoost**} & \multicolumn{2}{c}{**NHPSO-JTVAC-SVM**} \\ \hline \multirow{2}{*}{Assembly process performance data} & MAPE (\%) & 21.67 & 16.52 & **11.79** \\  & MAE & 1.50 & 1.16 & **0.89** \\  & RMSE & 1.85 & 1.43 & **1.23** \\ \hline \multirow{2}{*}{Pre-outfitting process performance data} & MAPE (\%) & 118.50 & 40.52 & **17.86** \\  & MAE & 2.63 & 1.49 & **0.96** \\  & RMSE & 3.23 & 2.07 & **1.91** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison with other machine models.  
Figure 9: NHPSO-JTVAC-SVM model’s predicted results of the (**a**) block assembly process performance test set and (**b**) block pre-outfitting process performance test set.  
used methods, the parameter selection in this paper provides clearer theoretical guidance. Additionally, in the process of searching for parameters, the NHPSO-JTVAC algorithm is superior in terms of performance. Furthermore, the experimental results indicated that the NHPSO-JTVAC-SVM prediction model has good prediction accuracy. Overall, the results indicated that the optimized model is better than other machine learning models.  
Note that the fitness function used in this study was the MAPE. Although the test error MAPE of the NHPSO-JTVAC model was better than other models, other performance metrics such as RMSE were worse than those of other models such as GOA-SVM and GA-SVM. To optimize the model further, we may develop an optimization algorithm that considers multi-fitness functions, an important aspect of future research.  
Conceptualization, J.H.W.; Methodology, J.H.W.; Software, H.Z.; Validation, H.Z.; Formal Analysis, H.Z.; Investigation, H.Z.; Data curation, H.Z.; Writing-original draft preparation, J.H.W., H.Z.; Writing-review & editing, H.Z.; Supervision, J.H.W.; Project administration, J.H.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the IoT- and AI-based development of Digital Twin for Block Assembly Process (grant number 20006978) of the Korean Ministry of Trade, Industry and Energy, and the mid-sized shipyard dock and quay planning integrated management system (grant number 20007834) of the Korean Ministry of Trade, Industry and Energy.  
Data Availability Statement: Data sharing not applicable.  
This research was supported by the following research projects:  
1. IoT and AI-based development of Digital Twin for Block Assembly Process (20006978) of the Korean Ministry of Trade, Industry and Energy.  
2. Mid-sized shipyard dock and quay planning integrated management system (20007834) of the Korean Ministry of Trade, Industry and Energy.  
All the authors declare no conflict of interest.