## 1 Introduction
[PERSON] [2] developed a fuzzy BPN to incorporate production-control expert judgments with expert opinions to enhance the performance of an existing crisp BPN for predicting the output times of wafer lots. [PERSON] et al. [32] presented a fuzzy modeling method that was evolved further with a GA for the due-date assignment problem in semiconductor manufacturing. [PERSON] and [PERSON] [6] proposed a fuzzy data-mining approach based on an innovative fuzzy BPN to determine the lower and upper bounds of job cycle times for wafer fabrication factories. [PERSON] [33] developed a fuzzy neural network-based fluctuation-smoothing rule to better estimate the remaining cycle time of a job at a wafer fabrication factory. Recently, many hybrid approaches [34; 35] based on machine learning and classification have been proposed to improve job cycle time prediction accuracy and accuracy by analyzing the data from semiconductor fabrication factories. [PERSON] [36] applied a fuzzy BPN approach by pre-classifying wafer lots with the k-means classifier before predicting the output time of a job. [PERSON] et al. [37] proposed two hybrid approaches with job record post-classification, namely (i) the equally divided method and (ii) the proportional-to-error method, where a job was post-classified by a BPN after the forecasting error was generated. [PERSON] [38] developed cycle-time prediction models by applying machine learning and data-mining methods, where the best neural network model obtained a higher prediction accuracy than that of the decision-tree method. [PERSON] and [PERSON] [39] proposed a fuzzy BPN approach to improve the accuracy of wafer-lot output-time prediction using a relatively small training set. [PERSON] and [PERSON] [10] designed big-data analytics to predict wafer-lot cycle times. [PERSON] [40] proposed a BPN-based hybrid approach to estimate job cycle times and determine the cycle-time range for a semiconductor manufacturing factory. [PERSON] and [PERSON] [41] estimated the cycle time of a job using a BPN after a nonlinear approach had been used to normalize job cycle times. Previous studies have used various hybrid approaches for job cycle time prediction [42; 43]. Generally, comparisons and trade-offs are made to select a suitable approach among the aforementioned categories. Analytical methods are inappropriate for job cycle time prediction because it is challenging to construct a complex semiconductor manufacturing system analytically. Excessive simulation time and the need for a large amount of data are the main disadvantages of PS [44]. Besides, wafer-lot priorities and machine-dispatching rules change in real-time in semiconductor manufacturing systems, limiting the applicability of statistical methods in predicting the cycle times of wafer lots [10]. ANNs, CBR, and fuzzy modeling methods can provide reasonable prediction accuracy with a relatively small amount of data [44]. Many recent results have shown that classification-based hybrid approaches can improve the accuracy of job cycle time prediction without requiring a large amount of data from semiconductor fabrication factories [44; 45]. To improve the performance of job cycle time prediction, we develop a new hybrid approach that combines data mining (FCM-based GA) and machine learning (BPN) methods. The classification method selection is critical because the quality of the resulting classification can indeed affect the accuracy of job cycle time prediction for semiconductor fabrication factories. However, it is not appropriate to classify the job records exclusively into several clusters. In other words, one job record can belong to multiple clusters with different membership values. In that case, different membership values to different clusters indicate the different amount of effects toward the corresponding clusters.