Abstract
An innovative classification and back-propagation-network tree (CABPN tree) approach is proposed in this study to estimate the cycle time of a job in a wafer fabrication factory, which is one of the most important tasks in controlling the wafer fabrication factory. The CABPN tree approach is an extension from the traditional classification and regression tree (CART) approach. In CART, the cycle times of jobs of the same branch are estimated with the same value, which is far from accurate. To tackle this problem, the CABPN tree approach replaces the constant estimate with variant estimates. To this end, the cycle times of jobs of the same branch are estimated with a BPN, and may be different. In this way, the estimation accuracy can be improved. In addition, to determine the optimal location of the splitting point on a node, the symmetric partition with incremental re-learning (SP-IR) algorithm is proposed and illustrated with an example. The applicability of the CABPN tree approach is shown with a real case. The experimental results supported its effectiveness over several existing methods.
Highlights
The cycle time of a job is the time required for the job to go through the factory
Chen [10] and Chang and Hsieh [11] have constructed back propagation networks (BPNs) to estimate the cycle time of a job based on the attributes of the job and the current factory conditions
Artificial neural networks such as BPN, radial basis function (RBF) network, and support vector regression (SVR) are effective methods to carry out nonlinear approximation
Summary
The cycle time (flow time, manufacturing lead time) of a job is the time required for the job to go through the factory. Chen [6] applied classification and regression tree (CART) to estimate the cycle time of each job in a wafer fabrication factory. Chen [10] and Chang and Hsieh [11] have constructed back propagation networks (BPNs) (or feed-forward neural networks, FNNs) to estimate the cycle time of a job based on the attributes of the job and the current factory conditions These studies indicated that linear methods are incapable of estimating the cycle time of a job, which supported the application of nonlinear methods such as ANNs. In addition, to improve the effectiveness of an ANN approach, classifying jobs before (or after) estimating the cycle times have been shown to be a viable strategy.
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