標題: Approximating alpha-cut operations approach for effective and efficient fuzzy analytic hierarchy process analysis
作者: Chen, Toly
Lin, Yu-Cheng
Chiu, Min-Chi
工業工程與管理學系
Department of Industrial Engineering and Management
關鍵字: Fuzzy analytic hierarchy process;Alpha-cut operation;Logarithmic function;Approximation
公開日期: 1-Dec-2019
摘要: Fuzzy analytic hierarchy process (FAHP) has been widely applied to multicriteria decision making (MCDM). However, deriving the fuzzy maximal eigenvalue and eigenvector of a fuzzy pairwise comparison matrix is a computationally intensive task. As a result, most existing FAHP methods estimate, rather than derive, the fuzzy maximal eigenvalue and weights. Therefore, the results are inaccurate. By contrast, the alpha-cut operations (ACO) method derives the fuzzy maximal eigenvalue and weights, but is time-consuming. To address these issues, the approximating alpha-cut operations (xACO) approach is proposed in this study. The proposed xACO approach does not enumerate all possible combinations of the a cuts of fuzzy pairwise comparison results, but approximates the membership functions of the fuzzy maximal eigenvalue and weights with logarithmic functions in the process. To evaluate the performance of the xACO approach, it was applied to two real cases. According to the experimental results, the xACO approach estimated the fuzzy maximal eigenvalue and weights effectively and efficiently based on less than 0.2% of the entire results. (C) 2019 Elsevier B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/j.asoc.2019.105855
http://hdl.handle.net/11536/153460
ISSN: 1568-4946
DOI: 10.1016/j.asoc.2019.105855
期刊: APPLIED SOFT COMPUTING
Volume: 85
起始頁: 0
結束頁: 0
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