The q-Rung Orthopair Hesitant Fuzzy Uncertain Linguistic Aggregation Operators and Their Application in Multi-Attribute Decision Making
This paper combines the q-rung orthopair hesitant fuzzy sets (q-ROHFSs) with the uncertain linguistic variables, and proposes the q-rung orthopair hesitant fuzzy uncertain linguistic sets (q-ROHFULSs). In addition, the Schweizer-Sklar T-norm is introduced, and a multi-attribute decision-making metho...
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doaj-49332796a90e4235b7e3bbb21499704f2021-03-30T03:46:32ZengIEEEIEEE Access2169-35362020-01-01818708418711310.1109/ACCESS.2020.30297859218930The q-Rung Orthopair Hesitant Fuzzy Uncertain Linguistic Aggregation Operators and Their Application in Multi-Attribute Decision MakingLin Huang0https://orcid.org/0000-0002-9177-6810Xiujiu Yuan1Yaojun Ren2Department of Basic Sciences, Air Force Engineering University, Xi’an, ChinaDepartment of Basic Sciences, Air Force Engineering University, Xi’an, ChinaDepartment of Basic Sciences, Air Force Engineering University, Xi’an, ChinaThis paper combines the q-rung orthopair hesitant fuzzy sets (q-ROHFSs) with the uncertain linguistic variables, and proposes the q-rung orthopair hesitant fuzzy uncertain linguistic sets (q-ROHFULSs). In addition, the Schweizer-Sklar T-norm is introduced, and a multi-attribute decision-making method based on the q-rung orthopair hesitant fuzzy uncertain linguistic Schweizer-Sklar aggregation operators is established. Firstly, based on the Schweizer-Sklar T-norm, the operational properties of q-rung orthopair hesitant fuzzy uncertain linguistic elements are defined, and the score function, accuracy function and ranking method of the q-rung orthopair hesitant fuzzy uncertain linguistic elements are proposed. Secondly, the q-rung orthopair hesitant fuzzy uncertain linguistic Schweizer-Sklar Bonferroni mean (BM) operator and geometric Bonferroni mean (GBM) operator, Maclaurin symmetric mean (MSM) operator and dual Maclaurin symmetric mean (DMSM) operator, Maclaurin mean (MM) operator and dual Maclaurin mean (DMM) operator are defined. The calculation formulas of the operators are given, the related properties are studied, and the special forms of the operators are discussed. Finally, a multi-attribute decision-making model based on the q-rung orthopair hesitant fuzzy uncertain linguistic aggregation operators is established, and the feasibility and effectiveness of the decision-making method are demonstrated through calculation examples and comparative analyses.https://ieeexplore.ieee.org/document/9218930/q-Rung orthopair hesitant fuzzy uncertain linguistic sets (q-ROHFULSs)Schweizer-Sklar T-normBonferroni mean (BM) operatorMaclaurin symmetric mean (MSM) operatorMaclaurin mean (MM) operatormulti-attribute decision making (MADM) |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lin Huang Xiujiu Yuan Yaojun Ren |
spellingShingle |
Lin Huang Xiujiu Yuan Yaojun Ren The q-Rung Orthopair Hesitant Fuzzy Uncertain Linguistic Aggregation Operators and Their Application in Multi-Attribute Decision Making IEEE Access q-Rung orthopair hesitant fuzzy uncertain linguistic sets (q-ROHFULSs) Schweizer-Sklar T-norm Bonferroni mean (BM) operator Maclaurin symmetric mean (MSM) operator Maclaurin mean (MM) operator multi-attribute decision making (MADM) |
author_facet |
Lin Huang Xiujiu Yuan Yaojun Ren |
author_sort |
Lin Huang |
title |
The q-Rung Orthopair Hesitant Fuzzy Uncertain Linguistic Aggregation Operators and Their Application in Multi-Attribute Decision Making |
title_short |
The q-Rung Orthopair Hesitant Fuzzy Uncertain Linguistic Aggregation Operators and Their Application in Multi-Attribute Decision Making |
title_full |
The q-Rung Orthopair Hesitant Fuzzy Uncertain Linguistic Aggregation Operators and Their Application in Multi-Attribute Decision Making |
title_fullStr |
The q-Rung Orthopair Hesitant Fuzzy Uncertain Linguistic Aggregation Operators and Their Application in Multi-Attribute Decision Making |
title_full_unstemmed |
The q-Rung Orthopair Hesitant Fuzzy Uncertain Linguistic Aggregation Operators and Their Application in Multi-Attribute Decision Making |
title_sort |
q-rung orthopair hesitant fuzzy uncertain linguistic aggregation operators and their application in multi-attribute decision making |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
This paper combines the q-rung orthopair hesitant fuzzy sets (q-ROHFSs) with the uncertain linguistic variables, and proposes the q-rung orthopair hesitant fuzzy uncertain linguistic sets (q-ROHFULSs). In addition, the Schweizer-Sklar T-norm is introduced, and a multi-attribute decision-making method based on the q-rung orthopair hesitant fuzzy uncertain linguistic Schweizer-Sklar aggregation operators is established. Firstly, based on the Schweizer-Sklar T-norm, the operational properties of q-rung orthopair hesitant fuzzy uncertain linguistic elements are defined, and the score function, accuracy function and ranking method of the q-rung orthopair hesitant fuzzy uncertain linguistic elements are proposed. Secondly, the q-rung orthopair hesitant fuzzy uncertain linguistic Schweizer-Sklar Bonferroni mean (BM) operator and geometric Bonferroni mean (GBM) operator, Maclaurin symmetric mean (MSM) operator and dual Maclaurin symmetric mean (DMSM) operator, Maclaurin mean (MM) operator and dual Maclaurin mean (DMM) operator are defined. The calculation formulas of the operators are given, the related properties are studied, and the special forms of the operators are discussed. Finally, a multi-attribute decision-making model based on the q-rung orthopair hesitant fuzzy uncertain linguistic aggregation operators is established, and the feasibility and effectiveness of the decision-making method are demonstrated through calculation examples and comparative analyses. |
topic |
q-Rung orthopair hesitant fuzzy uncertain linguistic sets (q-ROHFULSs) Schweizer-Sklar T-norm Bonferroni mean (BM) operator Maclaurin symmetric mean (MSM) operator Maclaurin mean (MM) operator multi-attribute decision making (MADM) |
url |
https://ieeexplore.ieee.org/document/9218930/ |
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