An Additive Consistency and Consensus Approach for Group Decision Making With Probabilistic Hesitant Fuzzy Linguistic Preference Relations and Its Application in Failure Criticality Analysis

In this article, probabilistic hesitant fuzzy linguistic preference relations (PHFLPRs) are proposed to present the qualitative pairwise preference information of decision makers (DMs) with hesitation and probability uncertainty assessments. The measurements and improvements of additive consistency...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2022-11, Vol.52 (11), p.12501-12513
Main Authors: Wang, Zhichao, Ran, Yan, Jin, Chuanxi, Chen, Yifan, Zhang, Genbao
Format: Article
Language:English
Subjects:
Additive consistency
Additives
consensus
Consistency
Decision making
Failure analysis
failure criticality analysis
Fuzzy sets
group decision making (GDM)
Indexes
Linguistics
Mathematical model
Optimization
Optimization models
probabilistic hesitant fuzzy linguistic preference relations (PHFLPRs)
Probabilistic logic
Statistical analysis
Uncertainty
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Summary:In this article, probabilistic hesitant fuzzy linguistic preference relations (PHFLPRs) are proposed to present the qualitative pairwise preference information of decision makers (DMs) with hesitation and probability uncertainty assessments. The measurements and improvements of additive consistency and consensus of PHFLPRs are investigated in group decision making (GDM). First, a new concept of probabilistic hesitant fuzzy linguistic term sets is defined. Second, the consistency and consensus measurements are established to survey the additive consistency and consensus levels of PHFLPRs. Subsequently, an optimization model is developed to improve the unacceptably additive consistent PHFLPR. By optimizing the unacceptable consensual PHFLPRs with repeating additive consistency improvement, the acceptably additive consistent and consensual PHFLPRs are obtained, based on which DMs' weights are determined objectively and then, the collective PHFLPR is aggregated from individual PHFLPRs. Alternatives' priority weights are derived from the collective PHFLPR as GDM. Finally, an example about failure criticality analysis is given, and a comparison analysis is presented.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2021.3072364