Trapezoidal cubic hesitant fuzzy aggregation operators and their application in group decision-making

Fahmi, Aliya; Abdullah, Saleem; Amin, Fazli; Ali, Asad; Ahmed, Rafay; Shakeel, Muhammad

doi:10.3233/jifs-181703

Cited by 24 publications

(6 citation statements)

References 21 publications

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“…Amin et al [54] defined some aggregation operators for triangular cubic linguistic hesitant fuzzy sets. Fahmi et al [55] defined some new operation laws for trapezoidal cubic hesitant fuzzy (TrCHF) numbers and introduced some new aggregation operators. Jiang et al [56] defined the concept of interval-valued dual HFS, and described aggregation operators under interval-valued dual HF environment based on Hamacher t-norm and t-conorm.…”

Section: Introductionmentioning

confidence: 99%

Hesitant T-spherical Dombi fuzzy aggregation operators and their applications in multiple criteria group decision-making

Karaaslan

Al-Husseinawi

2022

Complex Intell. Syst.

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A hesitant fuzzy (HF) set is an extension of the fuzzy sets and a T-spherical fuzzy set (T-SFS) is a generalization of the spherical fuzzy set (SFS). HF set has a significant role for modelling disagreements of the decision-makers over membership degree of an element. Also, T-SFS is quite effective in the modelling of the uncertainty for decision-making (DM) problems. In this paper, we define the concept of hesitant T-spherical fuzzy (HT-SF) set (HT-SFS) by combining concepts of HF set and T-SFS, and present some set-theoretical operations of HT-SFSs. We also develop the Dombi operations on HT-SFSs. We present some aggregation operators based on Dombi operators, including hesitant T-spherical Dombi fuzzy weighted arithmetic averaging operator, hesitant T-spherical Dombi fuzzy weighted geometric averaging operator, hesitant T-spherical Dombi fuzzy ordered weighted arithmetic averaging operator, and hesitant T-spherical Dombi fuzzy ordered weighted geometric averaging operator, and investigate some properties of them. In addition, we give a multi-criteria group decision-making method and algorithm of the proposed method under the hesitant T-spherical fuzzy environment. To show the process of proposed method, we present an example related to the selection of the most suitable person for the assistant professorship position in a university. Besides this, we present a comparative analysis with existing operators to reveal the advantages and authenticity of our technique.

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Section: Introductionmentioning

confidence: 99%

Hesitant T-spherical Dombi fuzzy aggregation operators and their applications in multiple criteria group decision-making

Karaaslan

Al-Husseinawi

2022

Complex Intell. Syst.

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“…Moreover, Fahmi et al [21] have introduced the idea of trapezoidal cubic hesitant fuzzy number and proposed a TOPSIS method. Fahmi et al [22] then has proposed some new operation laws for trapezoidal cubic hesitant fuzzy numbers and their aggregation operators and Fahmi et al [23] has defined some new operation laws for the trapezoidal linguistic cubic fuzzy number and Hamming distance of the numbers. Afterwards, Fahmi et al [24] have developed an MADM method based on trapezoidal cubic fuzzy numbers.…”

Section: Introductionmentioning

confidence: 99%

Not-ordered quasi-distance measures of generalised trapezoidal hesitant fuzzy numbers and their application to decision making problems

Deli̇

ÇELİK

2022

Journal of New Results in Science

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As an extension of the trapezoidal fuzzy number, the generalised trapezoidal hesitant fuzzy number is an effective mathematical tool for handling uncertainty and vagueness in decision-making problems. Considering that the quasi-distance measure has a strong ability to process and analyse data, we initiated some novel quasi-distance measures to measure the strength of the relationship between generalised trapezoidal hesitant fuzzy numbers in this paper. Moreover, based on the proposed measures, a new multi-criteria decision-making approach is proposed to address uncertain real-life situations. Finally, a practical application of the proposed approach is also illustrated to demonstrate the effectiveness and applicability.

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“…Fuzzy set theory has obtained plenty of achievements in the applications of science and engineering (Rafiq et al 2019;Ashraf et al 2019b). Notions and frames of fuzzy sets have also richly developed, such as Pythagorean fuzzy set (Khan et al 2019c, d, e), neutrosophic set (Ashraf et al 2019c), hesitant fuzzy set (Fahmi et al 2019), etc. These developed fuzzy sets and logics are widely used in many fields.…”

Section: Introductionmentioning

confidence: 99%

A modified method of generating Z-number based on OWA weights and maximum entropy

Tian

Kang

2020

Soft Comput

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How to generate Z -number is an important and open issue in the uncertain information processing of Z -number. In Kang et al. (Int J Intell Syst 33(8):1745-1755, 2018), a method of generating Z -number using OWA weight and maximum entropy is investigated. However, the meaning of the method in Kang et al. (2018) is not clear enough according to the definition of Z -number. Inspired by the methodology in Kang et al. ( 2018), we modify the method of determining Z -number based on OWA weights and maximum entropy, which is more clear about the meaning of Z -number. In addition, the model of generating Z -number under the environment of group decision making is well investigated based the modified model. Some numerical examples are used to illustrate the effectiveness of the proposed methodology.

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Trapezoidal cubic hesitant fuzzy aggregation operators and their application in group decision-making

Cited by 24 publications

References 21 publications

Hesitant T-spherical Dombi fuzzy aggregation operators and their applications in multiple criteria group decision-making

Hesitant T-spherical Dombi fuzzy aggregation operators and their applications in multiple criteria group decision-making

Not-ordered quasi-distance measures of generalised trapezoidal hesitant fuzzy numbers and their application to decision making problems

A modified method of generating Z-number based on OWA weights and maximum entropy

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