A Direct Method to Compare Bipolar LR Fuzzy Numbers

Ghanbari, Reza; Ghorbani-Moghadam, Khatere; Mahdavi–Amiri, Nezam

doi:10.1155/2018/9578270

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…Akramy and Arshadz (2019) introduced a ranking method of trapezoidal bipolar fuzzy numbers based on total ordering. Ghanbari et al (2018) proposed a new method for comparing bipolar LR fuzzy numbers. Chen et al (2014) introduced a

-polar fuzzy set, an extension of BF-sets.…”

Section: Introductionmentioning

confidence: 99%

Multi-criteria decision making of COVID-19 vaccines (in India) based on ranking interpreter technique under single valued bipolar neutrosophic environment

Garai¹,

Garg

2022

Expert Systems with Applications

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-polar fuzzy set, an extension of BF-sets.…”

Section: Introductionmentioning

confidence: 99%

Multi-criteria decision making of COVID-19 vaccines (in India) based on ranking interpreter technique under single valued bipolar neutrosophic environment

Garai¹,

Garg

2022

Expert Systems with Applications

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“…Further, it has been generalized to various forms such as intuitionistic fuzzy numbers, Pythagorean fuzzy numbers, Fermatean fuzzy numbers, Bipolar fuzzy numbers, etc. Various ranking procedures are available on the different classes of fuzzy and intuitionistic fuzzy numbers [1,2,3,5,6,7,8,9,10,11]. Bipolar fuzzy numbers are very much valuable for modelling problems with imprecise and incomplete information.…”

Section: Introductionmentioning

confidence: 99%

Ranking of Trapezoidal Bipolar Fuzzy Numbers Based on a New Improved Score Function

Jeevaraj¹

2021

Frontiers in Artificial Intelligence and Applications

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Bipolar fuzzy numbers plays a vital role in any Decision-making problem modelled under a bipolar fuzzy environment. In 2018, Akram and Arshad [1] introduced a new ranking function on the class of Trapezoidal Bipolar fuzzy numbers based on the area of the left and right membership function of a TrBFN, and they have discriminated any two TrBFNs by using it. The ranking principle introduced by Akram and Arshad [1] works better only when two bipolar fuzzy numbers have different rankings. We describe that the ranking function does not work with counterexamples when two or more bipolar fuzzy numbers have the same rankings. In this paper, we improve the ranking principle introduced in [1] by introducing a new Improved Score function. Firstly, we discuss the drawbacks and limitations of the ranking function introduced by Akram and Arshad [1]. Secondly, we introduce a new ranking function and study its properties. Thirdly, we introduce a new ranking principle by combining Akram and Arshad’s [1] ranking function and the proposed ranking function. Finally, we show the efficiency of the proposed ranking principle in comparing arbitrary TrBFNs.

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“…e gaps in the previous methods were because of their inability to cover human judgmental thinking that is on both positive and negative sides. is drawback has been overcome by the bipolar fuzzy concept see [17,18] which further innovates the new concept. On the other hand, by including soft sets, the order of preferences see, [19,20] is created; thus, a more precise result can be obtained.…”

Section: Introductionmentioning

confidence: 99%

A Study of Bipolar Fuzzy Soft Sets and Its Application in Decision-Making Problems

Mustafa

Safdar

Bibi

et al. 2021

Mathematical Problems in Engineering

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We live in a society where we have to deal with so many social issues daily, and making a right choice is everyone’s main concern. This study is based on the selection of right university while getting admission which is the core issue for students nowadays. We are concerned with bipolar fuzzy multicriteria decision-making methods. The main purpose is to provide guidance to the students for determining the best university and evaluating the factors that are affecting while getting admission. We thus combined bipolar fuzzy and soft expert sets to give multicriteria decision-making approach that overcomes the issues that arise while taking decisions. This study involves the development of structural hierarchical models of parameters and the implementation of soft expert sets to make the decision-making problem much more precise by introducing a new algorithm. Thus, this model is helpful for multicriteria decision making and can be used for university selection and thus suitable for education sector as well.

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A Direct Method to Compare Bipolar LR Fuzzy Numbers

Cited by 7 publications

References 12 publications

Multi-criteria decision making of COVID-19 vaccines (in India) based on ranking interpreter technique under single valued bipolar neutrosophic environment

Multi-criteria decision making of COVID-19 vaccines (in India) based on ranking interpreter technique under single valued bipolar neutrosophic environment

Ranking of Trapezoidal Bipolar Fuzzy Numbers Based on a New Improved Score Function

A Study of Bipolar Fuzzy Soft Sets and Its Application in Decision-Making Problems

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