Browsing by Author "Tong, L. I."

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A two-phase algorithm for product part change utilizing AHP and PSO
(2011-07-01)
Huang, P. C.
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Tong, L. I.
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Chang, W. W.
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Yeh, W. C.
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工業工程與管理學系
This study presents a two-phase algorithm approach to deal with the issue of product part change, and the issue of supplier selection derived from the former. In the first step, Analytical Hierarchy Process (AHP) was used on expert interview records to select the module in a product that needs to be changed with top priority. In the second step, after changing the module, the supplier selection process, including building a mathematical programming model, was initiated to select the best suppliers using Particle Swarm Optimization (PSO) algorithm. We tried to use this method to maximize the value of product updating so as to extend the product life cycle, under the conditions of limited resource, and keeping the scope of change to a minimum. Finally, we selected a switchboard manufacturer as a case study to test the proposed algorithm. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.
The Optimal Re-sampling Strategy for a Risk Assessment Model
(2012)
Tong, L. I.
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Wei, W. Y.
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Wu, P. Y.
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工業工程與管理學系
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Department of Industrial Engineering and Management
The global economic environment is changing rapidly. Consequently, the financial risks of banks or financial institutions are also increased. Banks or financial institutions often utilize various classification methods to construct risk assessment models to determine whether to grant loans to a corporation or an individual. It is often found that the data used to construct a risk assessment model are imbalanced. That is, the number of default is significantly smaller than the number of non-default. In this case, most classification methods fail to construct an accurate risk assessment model since the classification methods are subjected to the imbalanced data. The try-and-error method is often utilized to balance the sample sizes for default and non-default classes. However, the try-and error method is costly and the sampling strategy determined by the try-and-error method may not effectively classify the imbalanced data. Therefore, this study aims to develop an optimal re-sampling strategy using design of experiments (DOE) and dual response surface methodology (DRS). The proposed method can be employed for any classification method to develop a risk assessment model. The effectiveness of the proposed procedure is verified using a real case from a Taiwanese financial institution.
Uncertainty Assessment of Non-normal Emission Estimates Using Non-Parametric Bootstrap Confidence Intervals
(2016-09)
Tong, L. I.
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Saminathan, R.
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Chang, C. W.
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工業工程與管理學系
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Department of Industrial Engineering and Management
Mitigating global warming problems initially involves reducing greenhouse gas (GHG) emissions, therefore the uncertainty of GHG emission estimates needs to be assessed concisely. Although the uncertainty of GHG emission estimates is generally evaluated using classical confidence interval, quantifying the uncertainty based on non-normal GHG emission estimates or small dataset may lead to a significant bias. Using bootstrap confidence intervals is an effective means of reducing such a bias. This study presents a procedure for constructing four bootstrap confidence intervals to assess the uncertainty of GHG emission estimates for three non-normal distributions (namely, Weibull, Gamma and Beta). These bootstrap confidence intervals are standard bootstrap (SB) confidence interval, percentile bootstrap (PB) confidence interval, Bias-corrected percentiles bootstrap (BCPB) confidence interval and bias-corrected and accelerated (BCa) confidence interval. The sensitivity of bootstrap intervals for emission data is examined under various combinations of sample size and parameters of underlying non-normal distributions using three indices: coverage performance, interval mean, and interval standard deviation. Simulation results indicate that the bootstrap confidence intervals for assessing the uncertainty of emission estimates are highly applicable with small sample size and the data distribution is non-normal. Compared with the classical confidence interval, bootstrap confidence intervals have smaller interval mean and smaller interval standard deviation for small sample size under non-normal distributions. This study recommends BCa confidence interval to assess the uncertainty of the emission estimates as long as sample size is 15 or more and the distribution is non-normal. A case study with emission data of the High-Bleed Pneumatic controllers demonstrates the effectiveness of the proposed procedure