2021
DOI: 10.1002/int.22604
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Generalized hesitant fuzzy numbers: Introducing, arithmetic operations, aggregation operators, and an application

Abstract: Uncertainty modeling can be done by different theories, such as probability theory, fuzzy sets theory, vague sets theory, and so forth. Each of them may be appropriate for special cases and circumstances, based on the nature and type of the existing problem. In this

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Cited by 7 publications
(8 citation statements)
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“…Keikha ( 2021 ) For the universal set , positive real numbers , and doubtness/membership/satisfaction degrees , the generalized hesitant fuzzy number is defined as As it is seen, a GHFN similar to a HFN contain two parts: a real values part, and a hesitation degrees part. For any two GHFNs, if cardinalities of their real values parts are equal, and simultaneously the cardinalities of their hesitation degrees parts are also the same, they are called adjusted GHFNs (AGHFNs).…”
Section: Basic Concepts and Definitionsmentioning
confidence: 99%
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“…Keikha ( 2021 ) For the universal set , positive real numbers , and doubtness/membership/satisfaction degrees , the generalized hesitant fuzzy number is defined as As it is seen, a GHFN similar to a HFN contain two parts: a real values part, and a hesitation degrees part. For any two GHFNs, if cardinalities of their real values parts are equal, and simultaneously the cardinalities of their hesitation degrees parts are also the same, they are called adjusted GHFNs (AGHFNs).…”
Section: Basic Concepts and Definitionsmentioning
confidence: 99%
“…Keikha ( 2021 ) For each GHFNs as , its mean value, score, and variance , displayed by , , and , respectively, can be obtained as follows: …”
Section: Basic Concepts and Definitionsmentioning
confidence: 99%
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