## 4 Conclusions  
This study investigated the relationships between job attributes and job cycle time using a dataset obtained from a semiconductor fabrication factory. To enhance the effectiveness of the job cycle time prediction for semiconductor fabrication factories, a new hybrid approach is proposed, and it comprises FCM-based GA and BPN. Many previous studies used pre-classification or post-classification methods. However, such methods have several drawbacks, such as unequal sizes of different clusters. The FCM-based GA represents a new soft unsupervised classification method wherein the GA is used to improve the classification performance of the FCM. Hence, a good set of cluster centers are obtained while balancing the fuzzy memberships of data points to different centers by applying the FCM-based GA, which captures the main features of the dataset. The results obtained from the FCM-based GA are used as the training data and fed into the BPN predictor. The BPN predictor is then trained to predict the cycle time of new jobs with different attributes. We also presented a case study to demonstrate the effectiveness of the proposed approach. To obtain good job cycle time prediction performance, various parameters in the experiments were tested to achieve the best performance. The obtained results were compared to those of the previous research to show the dominating performance over the existing methodologies. These efforts lead us to the following conclusions: (1) The FCM-based GA can better pre-classify the first dataset of job records, enabling the BPN to produce accurate job cycle time predictions; (2) Stable and fuzzy job-record classification results allow the BPN to predict the job cycle time more accurately, as shown in Table 3; (3) The proposed hybrid approach indicates that classification-based methods are superior to those without job-record classification. The limitation of this study is explained here. The computational times were not obtained during the experiments since most studies using the single-layer ANN with the back propagation do not measure it meaningfully. More precisely, the computational times can be divided into (1) FCM clustering and GA, and (2) ANN training. The prediction time is almost instant. The computational times for FCM clustering and GA were varied by various parameters, including the number of centers, threshold values, and many GA parameters (shown in Table 1), which requires significant efforts in the design of experiments to optimize the performance. The training time depends on the size of the data and the training algorithm. Since the primary focus of this study was to propose a novel algorithm, the experimental design to understand the performance is beyond this study. Another limitation is also related to the optimization of the performance. A simple train/test split was used instead of k-fold cross-validation, which may help the best performance of the ANN training. The primary reason is why the goal of this study was to propose a novel methodology, not to optimize the proposed algorithms. In addition, since FCM clustering is used, we believe that its effects may be aggregated as a grouping in k-fold cross-validation. Furthermore, since we used the clustered c-means by the stochastic algorithm (i.e., GA) instead of the raw data, the benefits of the resampling by k-fold cross-validation may not be justified. However, the study on the benefits of the k-fold cross-validation in our proposed algorithm can be studied in the future. In future work, we plan to explore the following interesting problems. The optimal number of clusters can be identified by further research of preprocessing the dataset. Given their importance, the proposed approach could be modified to predict the lower and upper job cycle time bounds. In addition, fuzzy concepts could be incorporated into the BPN to enhance prediction accuracy. Another interesting direction for future work would be identifying and eliminating invalid job records because such noise data may affect prediction accuracy.