Novel Approach in Decision Making with m–Polar Fuzzy ELECTRE-I

Akram, Muhammad; Waseem, Neha; Лю, Пэйдэ

doi:10.1007/s40815-019-00608-y

Cited by 82 publications

(35 citation statements)

References 28 publications

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“…Several researchers explored and discussed MCDM methods based on COMET including [41][42][43]. For other concepts, terminologies, applications and MCDM methods, the readers are referred to [44][45][46][47][48][49][50][51][52][53][54][55].…”

Section: Introductionmentioning

confidence: 99%

Group Decision-Making Based on m-Polar Fuzzy Linguistic TOPSIS Method

2019

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The fuzzy linguistic approach provides favorable outputs in several areas, whose description is relatively qualitative. The encouragement for the utilization of sentences or words instead of numbers is that linguistic characterizations or classifications are usually less absolute than algebraic or arithmetical ones. In this research article, we animate the m-polar fuzzy (mF) linguistic approach and elaborate it with real life examples and tabular representation to develop the affluence of linguistic variables based on mF approach. As an extension of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, we develop an m-polar fuzzy linguistic TOPSIS approach for multi-criteria group decision-making (MCGDM). It is used to evaluate the best alternative, to get more authentic and comparable results and to handle the real life problems of having multi-polar information in terms of linguistic variables and values. In this approach decision-makers contribute their estimations in the form of linguistic term sets. To show the efficiency and compatibility of the proposed approach, we compare it with the m-polar fuzzy linguistic ELECTRE-I (Elimination and Choice Translating Reality) approach. Finally, we draw a flow chart of our proposed approach as an algorithm and generate a computer programming code.

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Section: Introductionmentioning

confidence: 99%

Group Decision-Making Based on m-Polar Fuzzy Linguistic TOPSIS Method

2019

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“…We observe that almost all AOs used BF numbers, intuitionistic fuzzy numbers, Pythagorean fuzzy numbers, or mF numbers without using Hamacher operations. Akram et al [25][26][27][28][29][30][31] introduced several decision-making techniques. In this research article, our main focus is how to apply Hamacher operators to aggregate the mF information.…”

Section: Introductionmentioning

confidence: 99%

Multi-Attribute Decision-Making Based on m-Polar Fuzzy Hamacher Aggregation Operators

2019

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In this paper, we introduce certain aggregation operators, namely, the m-polar fuzzy (mF) Hamacher weighted average operator, mF Hamacher ordered weighted average (mFHOWA) operator, mF Hamacher hybrid average (mFHHA) operator, mF Hamacher weighted geometric (mFHWG) operator, mF Hamacher weighted ordered geometric operator, and mF Hamacher hybrid geometric (mFHHG) operator. We discuss some properties of these operators, inclusive of their ability to implement both symmetric and asymmetric treatments of the items. We develop an algorithmic model to solve multi-attribute decision-making (MADM) problems in mF environment using mF Hamacher weighted average operator (mFHWA) and mFHWG operators. They can compensate for the possible asymmetric roles of the attributes that describe the problem. In the end, to prove the validity and feasibility of the proposed work, we give applications for selecting the most affected country regarding human trafficking, selecting health care waste treatment methods and selecting the best company for investment. We also solve practical MADM problems by using ELECTRE-I method, and give a comparative analysis.

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“…In 2014, Chen et al [26] introduced an m-polar fuzzy set, which is an extension of bipolar fuzzy sets. m-polar fuzzy sets have been applied to decision-making problems (see [27]), graph theory (see [28][29][30][31]), and BCK/BCI-algebra (see [32]).…”

Section: Introductionmentioning

confidence: 99%

Multipolar Fuzzy p-Ideals of BCI-Algebras

et al. 2019

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The notion of (normal) m-polar ( ∈ , ∈ ) -fuzzy p-ideals of BCI-algebras is introduced, and several properties are investigated. Relations between an m-polar ( ∈ , ∈ ) -fuzzy ideal and an m-polar ( ∈ , ∈ ) -fuzzy p-ideal are displayed, and conditions for an m-polar ( ∈ , ∈ ) -fuzzy ideal to be an m-polar ( ∈ , ∈ ) -fuzzy p-ideal are provided. Characterization of m-polar ( ∈ , ∈ ) -fuzzy p-ideals are considered. Given an m-polar ( ∈ , ∈ ) -fuzzy ideal (resp., m-polar ( ∈ , ∈ ) -fuzzy p-ideal), a normal m-polar ( ∈ , ∈ ) -fuzzy ideal (resp., normal m-polar ( ∈ , ∈ ) -fuzzy p-ideal) is established. Using an m-polar ( ∈ , ∈ ) -fuzzy ideal, the quotient structure of BCI-algebras is constructed.

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Novel Approach in Decision Making with m–Polar Fuzzy ELECTRE-I

Cited by 82 publications

References 28 publications

Group Decision-Making Based on m-Polar Fuzzy Linguistic TOPSIS Method

Group Decision-Making Based on m-Polar Fuzzy Linguistic TOPSIS Method

Multi-Attribute Decision-Making Based on m-Polar Fuzzy Hamacher Aggregation Operators

Multipolar Fuzzy p-Ideals of BCI-Algebras

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