The Symmetric-Partitioning and Incremental-Relearning Classification and Back-Propagation-Network Tree Approach for Cycle Time Estimation in Wafer Fabrication

Chen, Tin-Chih

doi:10.3390/sym6020409

Cited by 4 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The joint use of CART and BPN for estimating the cycle time of a job has rarely been discussed in this field. Chen [18] proposed a BPN tree approach in which the jobs of a branch are separated into two parts for either part a BPN is constructed to estimate the cycle times of jobs. However, Chen's approach relies on extensive and iterative BPN re-learning.…”

Section: Cycle Time Reductionmentioning

confidence: 99%

“…In addition, some hybrid methods including kM-FBPN [15], FCM-BPN and SOM-FBPN [12] are similar in nature to kM-BPN, and therefore were not compared. Further, the symmetric-partitioning and incremental-relearning classification and BPN approach proposed by Chen [18] was not compared because the execution time was more than 5 min. Only the estimation performances to the testing/validation data were compared.…”

Section: Dmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing the Effectiveness of Cycle Time Estimation in Wafer Fabrication-Efficient Methodology and Managerial Implications

Chen

Wang

2014

Sustainability

Self Cite

View full text Add to dashboard Cite

Cycle time management plays an important role in improving the performance of a wafer fabrication factory. It starts from the estimation of the cycle time of each job in the wafer fabrication factory. Although this topic has been widely investigated, several issues still need to be addressed, such as how to classify jobs suitable for the same estimation mechanism into the same group. In contrast, in most existing methods, jobs are classified according to their attributes. However, the differences between the attributes of two jobs may not be reflected on their cycle times. The bi-objective nature of classification and regression tree (CART) makes it especially suitable for tackling this problem. However, in CART, the cycle times of jobs of a branch are estimated with the same value, which is far from accurate. For these reason, this study proposes a joint use of principal component analysis (PCA), CART, and back propagation network (BPN), in which PCA is applied to construct a series of linear combinations of original variables to form new variables that are as unrelated to each other as possible. According to the new variables, jobs are classified using CART before estimating their cycle times with BPNs. A real case was used to evaluate the effectiveness of the proposed methodology. The experimental results supported the superiority of the proposed methodology over some existing methods. In addition, the managerial implications of the proposed methodology are also discussed with an example.

show abstract

Section: Cycle Time Reductionmentioning

confidence: 99%

Section: Dmentioning

confidence: 99%

Enhancing the Effectiveness of Cycle Time Estimation in Wafer Fabrication-Efficient Methodology and Managerial Implications

Chen

Wang

2014

Sustainability

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, predicting the cycle times of jobs in a wafer fabrication factory has received a lot of attention from production control, soft computing, and operations management researchers because of its critical role in the competitiveness of a wafer fabrication factory (Chen, 2013b;Chen, 2014). Cycle-time prediction is also related to job scheduling (Sivakumar, 1999;Tsai & Chen, 2013), due-date assignment (Joseph & Sridharan, 2011;Mor et al, 2013), and avail-to-promise (ATP) calculation (Oracle, 2014).…”

Section: Introductionmentioning

confidence: 99%

A new cloud computing method for establishing asymmetric cycle time intervals in a wafer fabrication factory

Chen

2015

J Intell Manuf

Self Cite

View full text Add to dashboard Cite

Abstract. Estimating the cycle time of each job in a wafer fabrication factory is critical.An equally critical problem is to estimate the range of a cycle time. This topic has rarely been discussed because most existing methods for range calculation establish only a symmetric range. However, a symmetric range does not meet the requirements of managerial applications based on the lower and upper bounds of a cycle time. Recently, a few researchers have attempted to establish the asymmetric bounds of a cycle time.However, these methods either have overly complex computations or do not consistently perform well. This study proposes a new approach for effective cycle-time bounding.First, construction of a back propagation network predicts the cycle time of a job. Second, two linear functions of the output are formed, and the threshold on the output node is fuzzified to derive the lower and upper bounds of the cycle time. In theory, such a treatment tightens the lower and upper bounds and improves the forecasting precision.Third, theorems for the feasibility of the two linear functions are proved. Fourth, a cloud computing scheme is proposed to improve the bounds in an effective manner. Finally, a real case illustrates the applicability of the proposed methodology. Experimental results show that this methodology narrows the range of cycle times for untrained data by 31%, while maintaining a considerably high hit rate of 92.5%.2

show abstract

Prediction of engine failure time using principal component analysis, categorical regression tree, and back propagation network

Wang

2018

J Ambient Intell Human Comput

View full text Add to dashboard Cite

The Symmetric-Partitioning and Incremental-Relearning Classification and Back-Propagation-Network Tree Approach for Cycle Time Estimation in Wafer Fabrication

Cited by 4 publications

References 16 publications

Enhancing the Effectiveness of Cycle Time Estimation in Wafer Fabrication-Efficient Methodology and Managerial Implications

Enhancing the Effectiveness of Cycle Time Estimation in Wafer Fabrication-Efficient Methodology and Managerial Implications

A new cloud computing method for establishing asymmetric cycle time intervals in a wafer fabrication factory

Prediction of engine failure time using principal component analysis, categorical regression tree, and back propagation network

Contact Info

Product

Resources

About