Browsing by Author "Chung, Pei-Han"

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Evaluation and Selection of Materials for Particulate Matter MEMS Sensors by Using Hybrid MCDM Methods
(2018-10-01)
Huang, Chi-Yo
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Chung, Pei-Han
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Shyu, Joseph Z.
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Ho, Yao-Hua
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Wu, Chao-Hsin
Air pollution poses serious problems as global industrialization continues to thrive. Since air pollution has grave impacts on human health, industry experts are starting to fathom how to integrate particulate matter (PM) sensors into portable devices; however, traditional micro-electro-mechanical systems (MEMS) gas sensors are too large. To overcome this challenge, experts from industry and academia have recently begun to investigate replacing the traditional etching techniques used on MEMS with semiconductor-based manufacturing processes and materials, such as gallium nitride (GaN), gallium arsenide (GaAs), and silicon. However, studies showing how to systematically evaluate and select suitable materials are rare in the literature. Therefore, this study aims to propose an analytic framework based on multiple criteria decision making (MCDM) to evaluate and select the most suitable materials for fabricating PM sensors. An empirical study based on recent research was conducted to demonstrate the feasibility of our analytic framework. The results provide an invaluable future reference for research institutes and providers.
Performance Evaluation of Medical Device Manufacturers Using a Hybrid Fuzzy MCDM
(2017-01)
Lee, Yen-Chun
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Chung, Pei-Han
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Shyu, Joseph Z.
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科技管理研究所
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Institute of Management of Technology
Fuzzy multi-criteria decision making (fuzzy MCDM) methods have developed rapidly and have evolved to accommodate various types of applications. This study aimed to explore a performance evaluation model based on a hybrid fuzzy MCDM approach for Taiwan\'s medical device Manufacturers. The hybrid fuzzy MCDM approach was composed of the fuzzy analytic hierarchy process (fuzzy AHP) method and the technique for order performance by similarity to ideal solution (fuzzy TOPSIS) method, to facilitate industrial practitioners on performance evaluation in a fuzzy environment. The proposed performance evaluation model can also enable government policy makers to better understand the complete evaluation process and provide a more effective decision support tool.