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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 11/2020

19.04.2019 | Original Article

Guaranteed-consensus posterior-aggregation fuzzy analytic hierarchy process method

verfasst von: Tin-Chih Toly Chen

Erschienen in: Neural Computing and Applications | Ausgabe 11/2020

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Abstract

Current group decision-making fuzzy analytic hierarchy processes (FAHPs) have two major problems. First, inconsistent fuzzy pairwise comparison results, rather than compromised fuzzy weights, are aggregated. Second, a consensus among decision makers (DMs) cannot be guaranteed. To address these problems, in this study, the guaranteed-consensus posterior-aggregation FAHP (GCPA-FAHP) method was proposed. In the proposed methodology, the membership functions of the linguistic terms for performing fuzzy pairwise comparisons were designed to guarantee a consensus among the DMs and can be modified afterward to enhance the estimation precision. In addition, fuzzy intersection and center of gravity were used to aggregate and defuzzify the estimated fuzzy weights. The GCPA-FAHP method was applied to a real case to evaluate its effectiveness. The experimental results revealed that the GCPA-FAHP method guaranteed consensus among the DMs and improved the precision of estimating fuzzy weights.
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Metadaten
Titel
Guaranteed-consensus posterior-aggregation fuzzy analytic hierarchy process method
verfasst von
Tin-Chih Toly Chen
Publikationsdatum
19.04.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 11/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04211-y

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