A new method for Atanassov’s interval-valued intuitionistic fuzzy MAGDM with incomplete attribute weight information

Wan, Shouhong; Xu, Guizhi; Wang, Feng; Dong, Jiuying

doi:10.1016/j.ins.2015.04.019

Cited by 110 publications

(45 citation statements)

References 49 publications

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“…Comparing with the existing approaches, our approach has more capability than [38,39] to model the vagueness in the practical MAGDM. Example A venture capital company desires to invest in a R&D project. After the market research and preliminary screening have been conducted, there are three potential R&D projects

{A_{1}, A_{2}, A_{3}}

for further evaluation.…”

Section: Two Numerical Examplesmentioning

confidence: 99%

Pythagorean Fuzzy Choquet Integral Based MABAC Method for Multiple Attribute Group Decision Making

Peng

Yang

2016

Int. J. Intell. Syst.

293

155

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In this paper, we define the Choquet integral operator for Pythagorean fuzzy aggregation operators, such as Pythagorean fuzzy Choquet integral average (PFCIA) operator and Pythagorean fuzzy Choquet integral geometric (PFCIG) operator. The operators not only consider the importance of the elements or their ordered positions but also can reflect the correlations among the elements or their ordered positions. It is worth pointing out that most of the existing Pythagorean fuzzy aggregation operators are special cases of our operators. Meanwhile, some basic properties are discussed in detail. Later, we propose two approaches to multiple attribute group decision making with attributes involving dependent and independent by the PFCIA operator and multi-attributive border approximation area comparison (MABAC) in Pythagorean fuzzy environment. Finally, two illustrative examples have also been taken in the present study to verify the developed approaches and to demonstrate their practicality and effectiveness. C 2016 Wiley Periodicals, Inc.

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{A_{1}, A_{2}, A_{3}}

for further evaluation.…”

Section: Two Numerical Examplesmentioning

confidence: 99%

Pythagorean Fuzzy Choquet Integral Based MABAC Method for Multiple Attribute Group Decision Making

Peng

Yang

2016

Int. J. Intell. Syst.

293

155

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“…IFS is characterized by membership degree and nonmembership degree and fulfill the condition that the sum of its membership and nonmembership degrees is less than or equal to 1. Form last few decades, the IFS has been explored by many researchers and widely applied to many practical fields like clustering analysis, decision‐making pattern recognition, and medical diagnosis . Xu proposed a series of aggregation operator’s, namely, intuitionistic fuzzy weighted average (IFWA) operator, intuitionistic fuzzy ordered weighted average (IFOWA) operator, and intuitionistic fuzzy hybrid (IFHA) operator under IFS environment.…”

Section: Introductionmentioning

confidence: 99%

Multiattribute group decision-making based on Pythagorean fuzzy Einstein prioritized aggregation operators

2018

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Pythagorean fuzzy set (PFS) is a powerful tool to deal with the imprecision and vagueness. Many aggregation operators have been proposed by many researchers based on PFSs. But the existing methods are under the hypothesis that the decision‐makers (DMs) and the attributes are at the same priority level. However, in real group decision‐making problems, the attribute and DMs may have different priority level. Therefore, in this paper, we introduce multiattribute group decision‐making (MAGDM) based on PFSs where there exists a prioritization relationship over the attributes and DMs. First we develop Pythagorean fuzzy Einstein prioritized weighted average operator and Pythagorean fuzzy Einstein prioritized weighted geometric operator. We study some of its desirable properties such as idempotency, boundary, and monotonicity in detail. Moreover we propose a MAGDM approach based on the developed operators under Pythagorean fuzzy environment. Finally, an illustrative example is provided to illustrate the practicality of the proposed approach.

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“…Zhang and Xu [45] constructed a goal-programming model to derive the weights of DMs. Wan et al [46] introduced a method for determining the DMs' weights based on the similarity and proximity degrees.…”

Section: Introductionmentioning

confidence: 99%

A projection-based approach to intuitionistic fuzzy group decision making

Yue

Jia

2017

Scientia Iranica

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Abstract. Group Decision Making (GDM) is usually used for solving complex decision problems, which is an important part of modern decision science. Weight of the Decision Maker (DM) plays an important role in the GDM process, and the projection-based approach is a comprehensive consideration between decision objects. It is a valuable work to determine the weights of DMs by a projection measurement. This paper investigates a GDM method based on projection measurement in an intuitionistic fuzzy environment. First, this article introduces an ideal decision among all individual decisions, and the weights of DMs are determined by using a projection measurement. Then, the individual decisions are aggregated into a collective decision. Finally, the preference order of alternatives is identi ed by using the score and accuracy function of the intuitionistic fuzzy numbers. In addition, a comparison with another GDM method is provided. Feasibility and practicability of the developed method are illustrated by an experimental analysis. The experimental result shows that the projection-based method is a high-resolution decision method.

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A new method for Atanassov’s interval-valued intuitionistic fuzzy MAGDM with incomplete attribute weight information

Cited by 110 publications

References 49 publications

Pythagorean Fuzzy Choquet Integral Based MABAC Method for Multiple Attribute Group Decision Making

Pythagorean Fuzzy Choquet Integral Based MABAC Method for Multiple Attribute Group Decision Making

Multiattribute group decision-making based on Pythagorean fuzzy Einstein prioritized aggregation operators

A projection-based approach to intuitionistic fuzzy group decision making

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