## 1 Introduction  
Job cycle time is the cycle time of a job or the time required to complete a job, the prediction of which is a critical task associated with different types of systems, such as production systems [1, 2], computer systems [3], and network systems [4]. Various managerial goals can be achieved once the job cycle time has been predicted accurately at a factory, including ordering-decision support, internal due-date assignment, output projection, enhancing customer relationships, guiding subsequent operations [5], pursuing sustainable development, meeting customer requirements, and promoting downstream operations. These advantages can support the competitiveness of a system to allow it to survive and be developed sustainably. In semiconductor manufacturing, each wafer fabrication factory is a complicated production system with idiosyncratic features such as changing demand, various product types and priorities, equipment unreliability, unbalanced capacity, job reentry into machines, alternative machines, sequence-dependent setup time, and shifting bottlenecks [6], which strongly affect job cycle time and make it very difficult to predict. However, these important features could be detected by mining and analyzing current job cycle time data. Herein, we present a hybrid approach that comprises the fuzzy c-means (FCM)-based genetic algorithm (GA) and a backpropagation network (BPN) to predict job cycle times for semiconductor manufacturing factories. Semiconductor wafer fabrication is currently a very complex manufacturing process, presenting various production planning and control issues. Predicting the cycle time of each job in a wafer fabrication factory is a critical task for every wafer manufacturer. [PERSON] [7] presented a closed-loop job-release control policy to minimize the average cycle time of jobs in a wafer fabrication factory. Various studies [2; 8; 9; 10] then emphasized the significance of predicting job cycle times for semiconductor fabrication factories. Here, we identify and review various studies that surveyed job cycle time prediction issues in semiconductor manufacturing. Initially, [PERSON] and [PERSON] [11] classified the existing approaches used to predict job cycle times in semiconductor manufacturing into four categories, namely (i) statistical, (ii) analytical, (iii) simulation, and (iv) hybrid methods. Now, because of the development of machine learning technology, six categories [2] are generally used, namely (i) statistical, (ii) production simulation (PS), (iii) artificial neural networks (ANNs), (iv) case-based reasoning (CBR), (v) fuzzy modeling, and (vi) hybrid approaches. Herein, we review previous studies in the context of (i) statistical analysis, (ii) analytical methods, (iii) ANNs, (iv) CBR, (v) PS, (vi) fuzzy modeling methods, and (vii) hybrid approaches. Statistical analysis is a prevalent method in practical applications. A regression model [12] was used to forecast the fab throughput time, including the wait time and processing time. [PERSON] et al. [13] used statistical methods based on modern data-mining algorithms to develop nonlinear predictors for estimating the cycle time of a target lot in a factory. [PERSON] et al. [14] proposed a nonlinear regression metamodel based on queuing theory to generate cycle-time-throughput curves, where simulation experiments were built up sequentially using a multistage procedure. Subsequently, [PERSON] et al. [15] used a factory simulation to fit the metamodels to determine the parameters of the generalized gamma distribution. [PERSON] et al. [16] presented a due-date assignment model by modeling the waiting time for each product type using a gamma distribution for the due-date assignment problem in a semiconductor fabrication factory. [PERSON] et al. [17] developed an accurate cycle-time estimation method to satisfy a targeted on-time delivery rate. A statistical approach was used to calculate the cycle time for the sum of multiple Weibull-distributed waiting times in the multilayer semiconductor final testing process.