G
Gwo-Hshiung Tzeng
Researcher at National Taipei University
Publications - 472
Citations - 30502
Gwo-Hshiung Tzeng is an academic researcher from National Taipei University. The author has contributed to research in topics: Multiple-criteria decision analysis & Fuzzy logic. The author has an hindex of 77, co-authored 465 publications receiving 26807 citations. Previous affiliations of Gwo-Hshiung Tzeng include Chung Yuan Christian University & National Chung Hsing University.
Papers
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Journal ArticleDOI
Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS
TL;DR: A comparative analysis of the multiple criteria decision making methods VIKOR and TOPSIS is illustrated with a numerical example, showing their similarity and some differences.
Book
Multiple Attribute Decision Making: Methods and Applications
Gwo-Hshiung Tzeng,Jih-Jeng Huang +1 more
TL;DR: A compilation of modern decision-making techniques, Multiple Attribute Decision Making: Methods and Applications focuses on the fuzzy set approach to multiple attribute decision making (MADM).
Journal ArticleDOI
Extended VIKOR method in comparison with outranking methods
TL;DR: The VIKOR method as mentioned in this paper was developed to solve MCDM problems with conflicting and noncommensurable (different units) criteria, assuming that compromising is acceptable for conflict resolution, the decision maker wants a solution that is the closest to the ideal, and the alternatives are evaluated according to all established criteria.
Journal ArticleDOI
Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL
TL;DR: Empirical experimental results show the proposed new novel hybrid MCDM model is capable of producing effective evaluation of e-learning programs with adequate criteria that fit with respondent's perception patterns, especially when the evaluation criteria are numerous and intertwined.
Journal ArticleDOI
Defuzzification within a multicriteria decision model
TL;DR: This model is applicable for defuzzification within the MCDM model with a mixed set of crisp and fuzzy criteria, and a newly developed CFCS method is based on the procedure of determining the left and right scores by fuzzy min and fuzzy max, respectively, and the total score is determined as a weighted average according to the membership functions.