Novel Multiple Criteria Group Decision-Making Method Based on Hesitant Fuzzy Clustering Algorithm

The aim of this paper is to investigate the multiple criteria group decision-making problem in which the evaluation values provided by experts construct some hesitant fuzzy numbers. We first analyze the characteristics of hesitant fuzzy number (HFN) and define the concept of feature vector of HFN. T...

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Vydané v:IEEE access Ročník 13; s. 15572 - 15584
Hlavní autori: Bian, Hongya, Li, Deqing, Liu, Yuang, Ma, Rong, Zeng, Wenyi, Xu, Zeshui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Clustering
Clustering algorithms
Decision making
Equivalence
feature vector of HFN
Fuzzy set theory
Fuzzy sets
Fuzzy systems
Hafnium
hesitant fuzzy clustering algorithm
Hesitant fuzzy number
Linguistics
MCDM
multiple criteria group decision making
Multiple criterion
Probabilistic logic
Q measurement
Similarity
Urban areas
Vectors
ISSN:2169-3536, 2169-3536
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Shrnutí:The aim of this paper is to investigate the multiple criteria group decision-making problem in which the evaluation values provided by experts construct some hesitant fuzzy numbers. We first analyze the characteristics of hesitant fuzzy number (HFN) and define the concept of feature vector of HFN. Then, applying the feature vectors of HFNs, some new similarity measures of HFNs are presented, which do not need to add elements to the HFN with fewer elements in the calculation process. Therefore, fuzzy similarity matrix is constructed, which is used to obtain a fuzzy equivalent matrix by using transitive closure method. By applying the fuzzy equivalent matrix, a novel hesitant fuzzy clustering algorithm is given. Furthermore, a new multiple criteria group decision making (MCGDM) algorithm is developed on the basis of the hesitant fuzzy clustering algorithm and the idea of ideal solution in multiple criteria decision making theory. To illustrate the effectiveness and feasibility of the developed MCGDM method, a numerical example is given and analyzed in detail. The results illustrate that the proposed method can provide more reasonable and credible rankings comparing with existing methods owing to keeping original data during computation.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3486370