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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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10735183/ |
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| author | Hongya Bian Deqing Li Yuang Liu Rong Ma Wenyi Zeng Zeshui Xu |
| author_facet | Hongya Bian Deqing Li Yuang Liu Rong Ma Wenyi Zeng Zeshui Xu |
| author_sort | Hongya Bian |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6f62569ab8264638a753ba8bdc43883f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6f62569ab8264638a753ba8bdc43883f2025-01-28T00:01:18ZengIEEEIEEE Access2169-35362025-01-0113155721558410.1109/ACCESS.2024.348637010735183Novel Multiple Criteria Group Decision-Making Method Based on Hesitant Fuzzy Clustering AlgorithmHongya Bian0Deqing Li1Yuang Liu2Rong Ma3Wenyi Zeng4https://orcid.org/0000-0002-8908-3329Zeshui Xu5https://orcid.org/0000-0003-3547-2908School of Data Science and Intelligent Engineering, Xiamen Institute of Technology, Xiamen, ChinaSchool of Data Science and Intelligent Engineering, Xiamen Institute of Technology, Xiamen, ChinaSchool of Artificial Intelligence, Beijing Normal University, Beijing, ChinaSchool of Artificial Intelligence, Beijing Normal University, Beijing, ChinaSchool of Artificial Intelligence, Beijing Normal University, Beijing, ChinaBusiness School, Sichuan University, Chengdu, ChinaThe 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.https://ieeexplore.ieee.org/document/10735183/Hesitant fuzzy numberfeature vector of HFNhesitant fuzzy clustering algorithmmultiple criteria group decision making |
| spellingShingle | Hongya Bian Deqing Li Yuang Liu Rong Ma Wenyi Zeng Zeshui Xu Novel Multiple Criteria Group Decision-Making Method Based on Hesitant Fuzzy Clustering Algorithm IEEE Access Hesitant fuzzy number feature vector of HFN hesitant fuzzy clustering algorithm multiple criteria group decision making |
| title | Novel Multiple Criteria Group Decision-Making Method Based on Hesitant Fuzzy Clustering Algorithm |
| title_full | Novel Multiple Criteria Group Decision-Making Method Based on Hesitant Fuzzy Clustering Algorithm |
| title_fullStr | Novel Multiple Criteria Group Decision-Making Method Based on Hesitant Fuzzy Clustering Algorithm |
| title_full_unstemmed | Novel Multiple Criteria Group Decision-Making Method Based on Hesitant Fuzzy Clustering Algorithm |
| title_short | Novel Multiple Criteria Group Decision-Making Method Based on Hesitant Fuzzy Clustering Algorithm |
| title_sort | novel multiple criteria group decision making method based on hesitant fuzzy clustering algorithm |
| topic | Hesitant fuzzy number feature vector of HFN hesitant fuzzy clustering algorithm multiple criteria group decision making |
| url | https://ieeexplore.ieee.org/document/10735183/ |
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