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Che-Jung Chang
TSL Business School, Quanzhou Normal University, No. 398, Donghai Street, Quanzhou, Fujian 362000, China

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Research article
Published: 10 February 2021 in Mathematical Problems in Engineering
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To cope with the increasingly fierce market competition environment, enterprises need to quickly respond to business issues and maintain business advantages, which require timely and correct decisions. In this context, the general mathematical modeling method may cause overfitting phenomenon when using small data sets, so it is difficult to ensure good analysis performance. Therefore, it is significant for enterprises to use limited samples to analyze and forecast. Over the past few decades, the grey model and its extensions have been shown to be effective tools for processing small data sets. To further enforce the effectiveness of data uncertainty processing, a fuzzy-decomposition modeling procedure for grey models is developed. Specifically, Latent Information (LI) function is employed to decompose the initial series into three subseries; next, the three subseries are used to build three grey models and create the estimated values of the three subseries; finally, the weighted average method is applying to combine the estimated values of the three subseries into a single final predicted value. After the actual test on the monthly demand data of the thin-film transistor liquid crystal display panels, the proposed fuzzy-decomposition modeling procedure can result in good prediction outcomes and is thus an appropriate decision analysis tool for managers.

ACS Style

Jianhong Guo; Che-Jung Chang; Yingyi Huang; Kun-Peng Yu. A Fuzzy-Decomposition Grey Modeling Procedure for Management Decision Analysis. Mathematical Problems in Engineering 2021, 2021, 1 -6.

AMA Style

Jianhong Guo, Che-Jung Chang, Yingyi Huang, Kun-Peng Yu. A Fuzzy-Decomposition Grey Modeling Procedure for Management Decision Analysis. Mathematical Problems in Engineering. 2021; 2021 ():1-6.

Chicago/Turabian Style

Jianhong Guo; Che-Jung Chang; Yingyi Huang; Kun-Peng Yu. 2021. "A Fuzzy-Decomposition Grey Modeling Procedure for Management Decision Analysis." Mathematical Problems in Engineering 2021, no. : 1-6.

Journal article
Published: 13 July 2019 in International Journal of Environmental Research and Public Health
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Effective determination of trends in sulfur dioxide emissions facilitates national efforts to draft an appropriate policy that aims to lower sulfur dioxide emissions, which is essential for reducing atmospheric pollution. However, to reflect the current situation, a favorable emission reduction policy should be based on updated information. Various forecasting methods have been developed, but their applications are often limited by insufficient data. Grey system theory is one potential approach for analyzing small data sets. In this study, an improved modeling procedure based on the grey system theory and the mega-trend-diffusion technique is proposed to forecast sulfur dioxide emissions in China. Compared with the results obtained by the support vector regression and the radial basis function network, the experimental results indicate that the proposed procedure can effectively handle forecasting problems involving small data sets. In addition, the forecast predicts a steady decline in China’s sulfur dioxide emissions. These findings can be used by the Chinese government to determine whether its current policy to reduce sulfur dioxide emissions is appropriate.

ACS Style

Che-Jung Chang; Guiping Li; Shao-Qing Zhang; Kun-Peng Yu. Employing a Fuzzy-Based Grey Modeling Procedure to Forecast China’s Sulfur Dioxide Emissions. International Journal of Environmental Research and Public Health 2019, 16, 2504 .

AMA Style

Che-Jung Chang, Guiping Li, Shao-Qing Zhang, Kun-Peng Yu. Employing a Fuzzy-Based Grey Modeling Procedure to Forecast China’s Sulfur Dioxide Emissions. International Journal of Environmental Research and Public Health. 2019; 16 (14):2504.

Chicago/Turabian Style

Che-Jung Chang; Guiping Li; Shao-Qing Zhang; Kun-Peng Yu. 2019. "Employing a Fuzzy-Based Grey Modeling Procedure to Forecast China’s Sulfur Dioxide Emissions." International Journal of Environmental Research and Public Health 16, no. 14: 2504.

Articles
Published: 03 April 2018 in Cybernetics and Systems
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When developing a production plan, accurate forecasting short-term demand is challenging for managers because a short forecast period indicates that the change in product demand exhibits an unsteady trend. Therefore, forecast models generated using a large amount of historical observations do not fully explain the data collected on developing patterns and, consequently, do not robustly forecast outcomes. However, if a low number of samples featuring the most recent information is used for developing a forecast, management efficiency could be enhanced and enterprises could gain a competitive advantage. To solve the problems associated with forecasting short-term demand when small datasets are available, we developed a residual discrete grey model that is based on modeling residual analysis. Specifically, we first applied the discrete grey model to create a forecasting model, and then used the obtained fitting residuals to generate training samples using the Latent Information function to learn the topology of a backpropagation neural network. Finally, the predictive errors obtained using the constructed network for adjusting the forecast to enhance the forecasting performance. We conducted an experiment using the demand data obtained from a thin film transistor liquid crystal display panel, and the results indicated that a highly accurate forecast could be obtained using the proposed modeling procedure. This finding suggests that the model developed in this study is a tool that enables short-term demand to be forecast accurately using small datasets.

ACS Style

Che-Jung Chang; Wen-Li Dai; Der-Chiang Li; Chien-Chih Chen. Latent-Function-Based Residual Discrete Grey Model for Short-Term Demand Forecasting. Cybernetics and Systems 2018, 49, 170 -180.

AMA Style

Che-Jung Chang, Wen-Li Dai, Der-Chiang Li, Chien-Chih Chen. Latent-Function-Based Residual Discrete Grey Model for Short-Term Demand Forecasting. Cybernetics and Systems. 2018; 49 (3):170-180.

Chicago/Turabian Style

Che-Jung Chang; Wen-Li Dai; Der-Chiang Li; Chien-Chih Chen. 2018. "Latent-Function-Based Residual Discrete Grey Model for Short-Term Demand Forecasting." Cybernetics and Systems 49, no. 3: 170-180.

Journal article
Published: 21 December 2016 in Journal of the Operational Research Society
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ACS Style

Che-Jung Chang; Liping Yu; Peng Jin. A mega-trend-diffusion grey forecasting model for short-term manufacturing demand. Journal of the Operational Research Society 2016, 67, 1439 -1445.

AMA Style

Che-Jung Chang, Liping Yu, Peng Jin. A mega-trend-diffusion grey forecasting model for short-term manufacturing demand. Journal of the Operational Research Society. 2016; 67 (12):1439-1445.

Chicago/Turabian Style

Che-Jung Chang; Liping Yu; Peng Jin. 2016. "A mega-trend-diffusion grey forecasting model for short-term manufacturing demand." Journal of the Operational Research Society 67, no. 12: 1439-1445.

Manuscript
Published: 09 September 2016 in Computational and Mathematical Organization Theory
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Product life cycles have become increasingly shorter because of global competition. Under fierce competition, the use of small samples to establish demand forecasting models is crucial for enterprises. However, limited samples typically cannot provide sufficient information; therefore, this presents a major challenge to managers who must determine demand development trends. To overcome this problem, this paper proposes a modified grey forecasting model, called DSI–GM(1,1). Specifically, we developed a data smoothing index to analyze the data behavior and rewrite the calculation equation of the background value in the applied grey modeling, constructing a suitable model for superior forecasting performance according to data characteristics. Employing a test on monthly demand data of thin film transistor liquid crystal display panels and the monthly average price of aluminum for cash buyers, the proposed modeling procedure resulted in high prediction outcomes; therefore, it is an appropriate tool for forecasting short-term demand with small samples.

ACS Style

Che-Jung Chang; Jan-Yan Lin; Peng Jin. A grey modeling procedure based on the data smoothing index for short-term manufacturing demand forecast. Computational and Mathematical Organization Theory 2016, 23, 409 -422.

AMA Style

Che-Jung Chang, Jan-Yan Lin, Peng Jin. A grey modeling procedure based on the data smoothing index for short-term manufacturing demand forecast. Computational and Mathematical Organization Theory. 2016; 23 (3):409-422.

Chicago/Turabian Style

Che-Jung Chang; Jan-Yan Lin; Peng Jin. 2016. "A grey modeling procedure based on the data smoothing index for short-term manufacturing demand forecast." Computational and Mathematical Organization Theory 23, no. 3: 409-422.

Journal article
Published: 01 April 2016 in Advanced Engineering Informatics
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Effectively forecasting the overall electricity consumption is vital for policy makers in rapidly developing countries. It can provide guidelines for planning electricity systems. However, common forecasting techniques based on large historical data sets are not applicable to these countries because their economic growth is high and unsteady; therefore, an accurate forecasting technique using limited samples is crucial. To solve this problem, this study proposes a novel modeling procedure. First, the latent information function is adopted to analyze data features and acquire hidden information from collected observations. Next, the projected sample generation is developed to extend the original data set for improving the forecasting performance of back propagation neural networks. The effectiveness of the proposed approach is estimated using three cases. The experimental results show that the proposed modeling procedure can provide valuable information for constructing a robust model, which yields precise predictions with the limited time series data. The proposed modeling procedure is useful for small time series forecasting.

ACS Style

Che-Jung Chang; Jan-Yan Lin; Meng-Jen Chang. Extended modeling procedure based on the projected sample for forecasting short-term electricity consumption. Advanced Engineering Informatics 2016, 30, 211 -217.

AMA Style

Che-Jung Chang, Jan-Yan Lin, Meng-Jen Chang. Extended modeling procedure based on the projected sample for forecasting short-term electricity consumption. Advanced Engineering Informatics. 2016; 30 (2):211-217.

Chicago/Turabian Style

Che-Jung Chang; Jan-Yan Lin; Meng-Jen Chang. 2016. "Extended modeling procedure based on the projected sample for forecasting short-term electricity consumption." Advanced Engineering Informatics 30, no. 2: 211-217.

Journal article
Published: 01 November 2015 in Journal of the Operational Research Society
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Small-data-set forecasting problems are a critical issue in various fields, with the early stage of a manufacturing system being a good example. Manufacturers require sufficient knowledge to minimize overall production costs, but this is difficult to achieve due to limited number of samples available at such times. This research was thus conducted to develop a modelling procedure to assist managers or decision makers in acquiring stable prediction results from small data sets. The proposed method is a two-stage procedure. First, we assessed some single models to determine whether the tendency of a real sequence can be reflected using grey incidence analysis, and we then evaluated their forecasting stability based on the relative ratio of error range. Second, a grey silhouette coefficient was developed to create an applicable hybrid forecasting model for small samples. Two real cases were analysed to confirm the effectiveness and practical value of the proposed method. The empirical results showed that the multimodel procedure can minimize forecasting errors and improve forecasting results with limited data. Consequently, the proposed procedure is considered a feasible tool for small-data-set forecasting problems. forecasting; grey theory; small data set; hybrid model Already a subscriber? Log in now or Register for online access. Members of the Operational Research Society receive access to Journal of the Operational Research Society as part of their membership. Access is available through the Operational Research Society web site.

ACS Style

Che-Jung Chang; Wen-Li Dai; Chien-Chih Chen. A novel procedure for multimodel development using the grey silhouette coefficient for small-data-set forecasting. Journal of the Operational Research Society 2015, 66, 1887 -1894.

AMA Style

Che-Jung Chang, Wen-Li Dai, Chien-Chih Chen. A novel procedure for multimodel development using the grey silhouette coefficient for small-data-set forecasting. Journal of the Operational Research Society. 2015; 66 (11):1887-1894.

Chicago/Turabian Style

Che-Jung Chang; Wen-Li Dai; Chien-Chih Chen. 2015. "A novel procedure for multimodel development using the grey silhouette coefficient for small-data-set forecasting." Journal of the Operational Research Society 66, no. 11: 1887-1894.

Journal article
Published: 01 August 2015 in Applied Mathematics and Computation
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Efficiently controlling the early stages of a manufacturing system is an important issue for enterprises. However, the number of samples collected at this point is usually limited due to time and cost issues, making it difficult to understand the real situation in the production process. One of the ways to solve this problem is to use a small data set forecasting tool, such as the various gray approaches. The gray model is a popular forecasting technique for use with small data sets, and while it has been successfully adopted in various fields, it can still be further improved. This paper thus uses a box plot to analyze data features and proposes a new formula for the background values in the gray model to improve forecasting accuracy. The new forecasting model is called BGM(1,1). In the experimental study, one public dataset and one real case are used to confirm the effectiveness of the proposed model, and the experimental results show that it is an appropriate tool for small data set forecasting. Small-data-set forecasting problem is difficult for most manufacturing environments.A forecasting tool using limited data for engineers and managers is more effective and efficient.The proposed method base on the box plot can analyze data features to improve forecasting accuracy with small data sets.The proposed method is considered an appropriate procedure in general to forecast manufacturing outputs based on small samples.

ACS Style

Che-Jung Chang; Der-Chiang Li; Yi-Hsiang Huang; Chien-Chih Chen. A novel gray forecasting model based on the box plot for small manufacturing data sets. Applied Mathematics and Computation 2015, 265, 400 -408.

AMA Style

Che-Jung Chang, Der-Chiang Li, Yi-Hsiang Huang, Chien-Chih Chen. A novel gray forecasting model based on the box plot for small manufacturing data sets. Applied Mathematics and Computation. 2015; 265 ():400-408.

Chicago/Turabian Style

Che-Jung Chang; Der-Chiang Li; Yi-Hsiang Huang; Chien-Chih Chen. 2015. "A novel gray forecasting model based on the box plot for small manufacturing data sets." Applied Mathematics and Computation 265, no. : 400-408.

Journal article
Published: 07 January 2015 in International Journal of Production Research
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ACS Style

Der-Chiang Li; Wen-Chih Chen; Che-Jung Chang; Chien-Chih Chen; I-Hsiang Wen. Practical information diffusion techniques to accelerate new product pilot runs. International Journal of Production Research 2015, 53, 5310 -5319.

AMA Style

Der-Chiang Li, Wen-Chih Chen, Che-Jung Chang, Chien-Chih Chen, I-Hsiang Wen. Practical information diffusion techniques to accelerate new product pilot runs. International Journal of Production Research. 2015; 53 (17):5310-5319.

Chicago/Turabian Style

Der-Chiang Li; Wen-Chih Chen; Che-Jung Chang; Chien-Chih Chen; I-Hsiang Wen. 2015. "Practical information diffusion techniques to accelerate new product pilot runs." International Journal of Production Research 53, no. 17: 5310-5319.

Journal article
Published: 01 October 2014 in Neurocomputing
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ACS Style

Der-Chiang Li; Wen-Ting Huang; Chien-Chih Chen; Che-Jung Chang. Employing box plots to build high-dimensional manufacturing models for new products in TFT-LCD plants. Neurocomputing 2014, 142, 73 -85.

AMA Style

Der-Chiang Li, Wen-Ting Huang, Chien-Chih Chen, Che-Jung Chang. Employing box plots to build high-dimensional manufacturing models for new products in TFT-LCD plants. Neurocomputing. 2014; 142 ():73-85.

Chicago/Turabian Style

Der-Chiang Li; Wen-Ting Huang; Chien-Chih Chen; Che-Jung Chang. 2014. "Employing box plots to build high-dimensional manufacturing models for new products in TFT-LCD plants." Neurocomputing 142, no. : 73-85.

Journal article
Published: 01 April 2014 in Neurocomputing
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ACS Style

Che-Jung Chang; Der-Chiang Li; Wen-Li Dai; Chien-Chih Chen. A latent information function to extend domain attributes to improve the accuracy of small-data-set forecasting. Neurocomputing 2014, 129, 343 -349.

AMA Style

Che-Jung Chang, Der-Chiang Li, Wen-Li Dai, Chien-Chih Chen. A latent information function to extend domain attributes to improve the accuracy of small-data-set forecasting. Neurocomputing. 2014; 129 ():343-349.

Chicago/Turabian Style

Che-Jung Chang; Der-Chiang Li; Wen-Li Dai; Chien-Chih Chen. 2014. "A latent information function to extend domain attributes to improve the accuracy of small-data-set forecasting." Neurocomputing 129, no. : 343-349.

Journal article
Published: 01 January 2014 in Computers & Industrial Engineering
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In the early stages of manufacturing systems, it is often difficult to obtain sufficient data to make accurate forecasts. Grey system theory is one of the approaches to deal with this issue, as it uses fairly small sets to construct forecasting models. Among published grey models, the current non-equigap grey models can deal with data having unequal gaps, and have been applied in various fields. However, these models usually use fixed modeling procedures that do not consider data growth trend differences. This paper utilizes the trend and potency tracking method to determine the parameter @a of the background value to build an adaptive non-equigap grey model to improve forecasting performance. The experimental results indicate that the proposed method considers that data occurrence properties can obtain better forecasting results.

ACS Style

Che-Jung Chang; Der-Chiang Li; Chien-Chih Chen; Chia-Sheng Chen. A forecasting model for small non-equigap data sets considering data weights and occurrence possibilities. Computers & Industrial Engineering 2014, 67, 139 -145.

AMA Style

Che-Jung Chang, Der-Chiang Li, Chien-Chih Chen, Chia-Sheng Chen. A forecasting model for small non-equigap data sets considering data weights and occurrence possibilities. Computers & Industrial Engineering. 2014; 67 ():139-145.

Chicago/Turabian Style

Che-Jung Chang; Der-Chiang Li; Chien-Chih Chen; Chia-Sheng Chen. 2014. "A forecasting model for small non-equigap data sets considering data weights and occurrence possibilities." Computers & Industrial Engineering 67, no. : 139-145.

Conference paper
Published: 01 November 2013 in Proceedings of 2013 IEEE International Conference on Grey systems and Intelligent Services (GSIS)
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Small sample forecasting problem is an important issue in various fields. The early stage of manufacturing system is a positive example about this issue. Manufacturers need sufficient management knowledge to lower overall production cost, but it is a hard task due to the obtained samples is limited. This study is thus to develop a modeling procedure to acquire stable prediction results under small data sets. Briefly, we first judge some single models to determine whether the real sequence tendency can be reflected with the grey incidence analysis and then evaluate their forecasting stability by the relative ratio of error range; finally, the grey silhouette coefficient is developed to build an applicable hybrid forecasting model for small samples. The material fatigue limit data set is used here to confirm the effectiveness and practical application value of the proposed method. The empirical results show that the hybrid model indeed can lower forecasting errors and come up better results with the limited data. Consequently, the proposed procedure is considered a feasible tool for the small sample forecasting problem.

ACS Style

Che-Jung Chang; Wen-Li Dai. A grey silhouette coefficient for the small sample forecasting. Proceedings of 2013 IEEE International Conference on Grey systems and Intelligent Services (GSIS) 2013, 81 -83.

AMA Style

Che-Jung Chang, Wen-Li Dai. A grey silhouette coefficient for the small sample forecasting. Proceedings of 2013 IEEE International Conference on Grey systems and Intelligent Services (GSIS). 2013; ():81-83.

Chicago/Turabian Style

Che-Jung Chang; Wen-Li Dai. 2013. "A grey silhouette coefficient for the small sample forecasting." Proceedings of 2013 IEEE International Conference on Grey systems and Intelligent Services (GSIS) , no. : 81-83.

Research article
Published: 17 July 2013 in Mathematical Problems in Engineering
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The wafer-level packaging process is an important technology used in semiconductor manufacturing, and how to effectively control this manufacturing system is thus an important issue for packaging firms. One way to aid in this process is to use a forecasting tool. However, the number of observations collected in the early stages of this process is usually too few to use with traditional forecasting techniques, and thus inaccurate results are obtained. One potential solution to this problem is the use of grey system theory, with its feature of small dataset modeling. This study thus uses the AGM(1,1) grey model to solve the problem of forecasting in the pilot run stage of the packaging process. The experimental results show that the grey approach is an appropriate and effective forecasting tool for use with small datasets and that it can be applied to improve the wafer-level packaging process.

ACS Style

Che-Jung Chang; Der-Chiang Li; Wen-Li Dai; Chien-Chih Chen. Utilizing an Adaptive Grey Model for Short-Term Time Series Forecasting: A Case Study of Wafer-Level Packaging. Mathematical Problems in Engineering 2013, 2013, 1 -6.

AMA Style

Che-Jung Chang, Der-Chiang Li, Wen-Li Dai, Chien-Chih Chen. Utilizing an Adaptive Grey Model for Short-Term Time Series Forecasting: A Case Study of Wafer-Level Packaging. Mathematical Problems in Engineering. 2013; 2013 (5):1-6.

Chicago/Turabian Style

Che-Jung Chang; Der-Chiang Li; Wen-Li Dai; Chien-Chih Chen. 2013. "Utilizing an Adaptive Grey Model for Short-Term Time Series Forecasting: A Case Study of Wafer-Level Packaging." Mathematical Problems in Engineering 2013, no. 5: 1-6.

Articles
Published: 03 January 2013 in International Journal of Production Research
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Machine learning algorithms are widely applied to extract useful information, but the sample size is often an important factor in determining their reliability. The key issue that makes small dataset learning tasks difficult is that the information that such datasets contain cannot fully represent the characteristics of the entire population. The principal approach of this study to overcome this problem is systematically adding artificial samples to fill the data gaps; this research employs the mega-trend-diffusion technique to generate virtual samples to extend the data size. In this paper, a real, small dataset learning task in the array process of a thin-film transistor liquid-crystal display (TFT-LCD) panel manufacturer is proposed, where there are only 20 samples used for learning the relationship between 15 inputs and 36 output attributes. The experiment results show that the approach is effective in building robust back-propagation neural network (BPN) and support vector regression (SVR) models. In addition, a sensitivity analysis is implemented with the 20 samples by using SVR to extract the relationship between the 15 factors and the 36 outputs to help engineers infer process knowledge.

ACS Style

Der-Chiang Li; Wen-Ting Huang; Chien-Chih Chen; Che-Jung Chang. Employing virtual samples to build early high-dimensional manufacturing models. International Journal of Production Research 2013, 51, 3206 -3224.

AMA Style

Der-Chiang Li, Wen-Ting Huang, Chien-Chih Chen, Che-Jung Chang. Employing virtual samples to build early high-dimensional manufacturing models. International Journal of Production Research. 2013; 51 (11):3206-3224.

Chicago/Turabian Style

Der-Chiang Li; Wen-Ting Huang; Chien-Chih Chen; Che-Jung Chang. 2013. "Employing virtual samples to build early high-dimensional manufacturing models." International Journal of Production Research 51, no. 11: 3206-3224.

Journal article
Published: 31 December 2012 in Omega
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ACS Style

Der-Chiang Li; Che-Jung Chang; Chien-Chih Chen; Wen-Chih Chen. Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case. Omega 2012, 40, 767 -773.

AMA Style

Der-Chiang Li, Che-Jung Chang, Chien-Chih Chen, Wen-Chih Chen. Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case. Omega. 2012; 40 (6):767-773.

Chicago/Turabian Style

Der-Chiang Li; Che-Jung Chang; Chien-Chih Chen; Wen-Chih Chen. 2012. "Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case." Omega 40, no. 6: 767-773.

Original articles
Published: 01 December 2012 in International Journal of Production Research
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In most highly competitive manufacturing industries, the sample sizes of new products are usually very small in pilot runs because the production schedules are very tight. To obtain the expected quality in mass production runs using limited data is, therefore, always a challenging issue for engineers. Although machine learning algorithms are widely applied to this task, the training sample size is a key weakness when determining the manufacturing parameters. In order to extract more robust information for engineers from the small datasets, this research, based on regression analysis and fuzzy techniques, develops an effective procedure for new production pattern constructions. In addition, a case study of TFT-LCD manufacturing in 2009 is taken as an example to illustrate the presented approach. The experimental results show that it is possible to develop a robust forecasting model which can provide more precise manufacturing predictions with the limited data acquired from pilot runs.

ACS Style

Der-Chiang Li; Chien-Chih Chen; Wen-Chih Chen; Che-Jung Chang. Employing dependent virtual samples to obtain more manufacturing information in pilot runs. International Journal of Production Research 2012, 50, 6886 -6903.

AMA Style

Der-Chiang Li, Chien-Chih Chen, Wen-Chih Chen, Che-Jung Chang. Employing dependent virtual samples to obtain more manufacturing information in pilot runs. International Journal of Production Research. 2012; 50 (23):6886-6903.

Chicago/Turabian Style

Der-Chiang Li; Chien-Chih Chen; Wen-Chih Chen; Che-Jung Chang. 2012. "Employing dependent virtual samples to obtain more manufacturing information in pilot runs." International Journal of Production Research 50, no. 23: 6886-6903.

Journal article
Published: 01 October 2012 in Applied Mathematical Modelling
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ACS Style

Der-Chiang Li; Che-Jung Chang; Chien-Chih Chen; Wen-Chih Chen. A grey-based fitting coefficient to build a hybrid forecasting model for small data sets. Applied Mathematical Modelling 2012, 36, 5101 -5108.

AMA Style

Der-Chiang Li, Che-Jung Chang, Chien-Chih Chen, Wen-Chih Chen. A grey-based fitting coefficient to build a hybrid forecasting model for small data sets. Applied Mathematical Modelling. 2012; 36 (10):5101-5108.

Chicago/Turabian Style

Der-Chiang Li; Che-Jung Chang; Chien-Chih Chen; Wen-Chih Chen. 2012. "A grey-based fitting coefficient to build a hybrid forecasting model for small data sets." Applied Mathematical Modelling 36, no. 10: 5101-5108.

Journal article
Published: 31 March 2012 in Expert Systems with Applications
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Determining manufacturing parameters for a new product is fundamentally a difficult problem, because there has little suggestion information. There are several researches on this topic, and most of them focus on single specific model or the engineer's experience. As to other approaches, the usage of multiple models may be an alternative approach to help determining the parameters. This research proposed an aggregation of multiple regression and back-propagation neural network to find the manufacturing parameter's limits (upper and lower limits). A real-problem of a new product parameter setting model in the real Thin Film Transistor-Liquid Crystal Display (TFT-LCD) manufacturing company is demonstrated, where three forecasting models are applied, and t test is used to judge which models are the suitable ones. Finally, we average the computed parameter values from the chosen models to suppress the system variance. The empirical results show that the proposed method is successful in suppressing the system variance and improving the production yields.

ACS Style

Der-Chiang Li; Wen-Chih Chen; Chiao-Wen Liu; Che-Jung Chang; Chien-Chih Chen. Determining manufacturing parameters to suppress system variance using linear and non-linear models. Expert Systems with Applications 2012, 39, 4020 -4025.

AMA Style

Der-Chiang Li, Wen-Chih Chen, Chiao-Wen Liu, Che-Jung Chang, Chien-Chih Chen. Determining manufacturing parameters to suppress system variance using linear and non-linear models. Expert Systems with Applications. 2012; 39 (4):4020-4025.

Chicago/Turabian Style

Der-Chiang Li; Wen-Chih Chen; Chiao-Wen Liu; Che-Jung Chang; Chien-Chih Chen. 2012. "Determining manufacturing parameters to suppress system variance using linear and non-linear models." Expert Systems with Applications 39, no. 4: 4020-4025.

Original articles
Published: 15 March 2012 in International Journal of Production Research
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Product life cycles are becoming shorter, especially in the optoelectronics industry. Shortening production cycle times using knowledge obtained in pilot runs, where sample sizes are usually very small, is thus becoming a core competitive ability for firms. Machine learning algorithms are widely applied to this task, but the number of training samples is always a key factor in determining their knowledge acquisition capability. Therefore, this study, based on box-and-whisker plots, systematically generates more training samples to help gain more knowledge in the early stages of manufacturing systems. A case study of a TFT-LCD manufacturer is taken as an example when a new product was phased-in in 2008. The experimental results show that it is possible to rapidly develop a production model that can provide more information and precise predictions with the limited data acquired from pilot runs.

ACS Style

Der-Chiang Li; Chien-Chih Chen; Che-Jung Chang; Wen-Chih Chen. Employing box-and-whisker plots for learning more knowledge in TFT-LCD pilot runs. International Journal of Production Research 2012, 50, 1539 -1553.

AMA Style

Der-Chiang Li, Chien-Chih Chen, Che-Jung Chang, Wen-Chih Chen. Employing box-and-whisker plots for learning more knowledge in TFT-LCD pilot runs. International Journal of Production Research. 2012; 50 (6):1539-1553.

Chicago/Turabian Style

Der-Chiang Li; Chien-Chih Chen; Che-Jung Chang; Wen-Chih Chen. 2012. "Employing box-and-whisker plots for learning more knowledge in TFT-LCD pilot runs." International Journal of Production Research 50, no. 6: 1539-1553.