Preliminary cost estimates for thin-film transistor liquid-crystal display inspection and repair equipment: A hybrid hierarchical approach

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题名:	Preliminary cost estimates for thin-film transistor liquid-crystal display inspection and repair equipment: A hybrid hierarchical approach
作者:	蔡志豐;Chou, Jui-Sheng;Tsai, Chih-Fong
贡献者:	管理學院資訊管理學系
关键词:	Accuracy;Classification;Cost estimates;Cost estimation;Equipment costs;Hierarchical classification;Inspection and repair equipment;LCDs;Learning theory;Liquid crystal displays;Mathematical models;Neural networks;Operations research;Regression;Repair & maintenance;Studies;TFT-LCD;Thin films;Transistors
日期:	2012-03-01
上传时间:	2026-04-23 13:50:35 (UTC+8)
出版者:	Elsevier Ltd.;New York: Elsevier Ltd
摘要:	摘要： ► A hybrid hierarchical cost prediction approach is introduced. ► This approach is used to predict the costs of TFT-LCD inspection and repair equipment. ► This approach has shown its superiority over single flat regression models. ► Support vector machines combined with support vector regression perform best. The thin-film transistor liquid–crystal display (TFT-LCD) industry has developed rapidly in recent years. Because TFT-LCD manufacturing is highly complex and requires different tools for different products, accurately estimating the cost of manufacturing TFT-LCD equipment is essential. Conventional cost estimation models include linear regression (LR), artificial neural networks (ANNs), and support vector regression (SVR). Nevertheless, in accordance with recent evidence that a hierarchical structure outperforms a flat structure, this study proposes a hierarchical classification and regression (HCR) approach for improving the accuracy of cost predictions for TFT-LCD inspection and repair equipment. Specifically, first-level analyses by HCR classify new unknown cases into specific classes. The cases are then inputted into the corresponding prediction models for the final output. In this study, experimental results based on a real world dataset containing data for TFT-LCD equipment development projects performed by a leading Taiwan provider show that three prediction models based on HCR approach are generally comparable or better than three conventional flat models (LR, ANN, and SVR) in terms of prediction accuracy. In particular, the 4-class and 5-class support vector machines in the first-level HCR combined with individual SVR obtain the lowest root mean square error (RMSE) and mean average percentage error (MAPE) rates, respectively. 出版者： New York: Elsevier Ltd 出版日期： 2012-03-01 出處： Computers & industrial engineering, 2012-03, Vol.62 (2), p.661-669 資源來源： Elsevier ScienceDirect Journals 版權： 2011 Elsevier Ltd 版權： Copyright Pergamon Press Inc. Mar 2012 識別號： ISSN: 0360-8352 識別號： EISSN: 1879-0550 識別號： DOI: 10.1016/j.cie.2011.11.037 識別號： CODEN: CINDDL
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