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

Adeel, Arooj; Akram, Muhammad; Koam, Ali N. A.

doi:10.3390/sym11060735

Cited by 60 publications

(47 citation statements)

References 52 publications

(51 reference statements)

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“…Aggregation of expert assessments on criteria in accordance with Equation (1) was also used in [39][40][41], where as part of creating hybrid approaches, the fuzzy TOPSIS method was combined with the Fuzzy AHP method. Examples of other fuzzy TOPSIS implementations using the technique of average value Shown in Equations (1) and (2) with TrFNs or trapezoidal fuzzy numbers (TFNs) were published in [42][43][44].…”

Section: Literature Reviewmentioning

confidence: 99%

“…An important disadvantage of both techniques is the fact that the agreement degree of expert opinions is not taken into account (expert consensus). average value Shown in Equations (1) and (2) with TrFNs or trapezoidal fuzzy numbers (TFNs) were published in [42][43][44]. Another way to aggregate expert assessments and criteria weights expressed using TFNs, evaluation = , , , , and weight = , , , was presented by Chen et al [11].…”

Section: Literature Reviewmentioning

confidence: 99%

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A Consensus Measure of Expert Judgment in the Fuzzy TOPSIS Method

Ziemba

Becker

2020

Symmetry

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In the case of many complex, real-world decision problems solved with the participation of a group of experts, it is important to capture the uncertainty of opinions and preferences expressed. In such situations, one can use many modifications of the technique for order preference by similarity to the ideal solution (TOPSIS) method, for example, based on fuzzy numbers. In fuzzy TOPSIS, two aggregation methods of fuzzy expert opinions dominate, the first based on the average value technique and the second one extended by the minimum and maximum functions for determining the support of the aggregated fuzzy number. An important disadvantage of both techniques is the fact that the agreement degree of expert opinions is not taken into account. This article proposes the inclusion of the modified procedure for aggregating individual expert opinions, taking into account the degree of agreement of their opinions (called the similarity aggregation method—SAM) and the ranking of experts into the fuzzy TOPSIS method. The fuzzy TOPSIS method extended in this way was used to solve the decision problem of recruiting employees by a group of experts. As part of the solution, the modified SAM was compared with aggregation procedures based on the average value and min-max (minimum and maximum) support. The results of the conducted research indicate that SAM allows fuzzy numbers to be obtained, characterized by less imprecision and greater stability than the other two considered aggregation procedures.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A Consensus Measure of Expert Judgment in the Fuzzy TOPSIS Method

Ziemba

Becker

2020

Symmetry

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“…A k-polar intuitionistic fuzzy set ( E , K) over F is called a k-polar intuitionistic fuzzy ideal of F if it satisfies the condition (19) and…”

Section: Definitionmentioning

confidence: 99%

“…An m-polar fuzzy set, an extension of a bipolar fuzzy set, was introduced by Chen et al [9] in 2014. After that, this concept was applied to BCK/BCI-algebra (see [10][11][12]), graph theory (see [13][14][15][16]), and decision making problems (see [17][18][19][20]).…”

Section: Introductionmentioning

confidence: 99%

Multipolar Intuitionistic Fuzzy Set with Finite Degree and Its Application in BCK/BCI-Algebras

2020

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When events occur in everyday life, it is sometimes advantageous to approach them in two directions to find a solution for them. As a mathematical tool to handle these things, we can consider the intuitionistic fuzzy set. However, when events are complex and the key to a solution cannot be easily found, we feel the need to approach them for hours and from various directions. As mathematicians, we wish we had the mathematical tools that apply to these processes. If these mathematical tools were developed, we would be able to apply them to algebra, topology, graph theory, etc., from a close point of view, and we would be able to apply these research results to decision-making and/or coding theory, etc., from a distant point of view. In light of this view, the purpose of this study is to introduce the notion of a multipolar intuitionistic fuzzy set with finite degree (briefly, k-polar intuitionistic fuzzy set), and to apply it to algebraic structure, in particular, a BCK/BCI-algebra. The notions of a k-polar intuitionistic fuzzy subalgebra and a (closed) k-polar intuitionistic fuzzy ideal in a BCK/BCI-algebra are introduced, and related properties are investigated. Relations between a k-polar intuitionistic fuzzy subalgebra and a k-polar intuitionistic fuzzy ideal are discussed. Characterizations of a k-polar intuitionistic fuzzy subalgebra/ideal are provided, and conditions for a k-polar intuitionistic fuzzy subalgebra to be a k-polar intuitionistic fuzzy ideal are provided. In a BCI-algebra, relations between a k-polar intuitionistic fuzzy ideal and a closed k-polar intuitionistic fuzzy ideal are discussed. A characterization of a closed k-polar intuitionistic fuzzy ideal is considered, and conditions for a k-polar intuitionistic fuzzy ideal to be closed are provided.

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“…We also present a real-life application to show the validity of our proposed reduction algorithms. For other terminologies not mentioned in the paper, the readers are referred to [31][32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Parameter Reductions of Bipolar Fuzzy Soft Sets with Their Decision-Making Algorithms

et al. 2019

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Parameter reduction is a very important technique in many fields, including pattern recognition. Many reduction techniques have been reported for fuzzy soft sets to solve decision-making problems. However, there is almost no attention to the parameter reduction of bipolar fuzzy soft sets, which take advantage of the fact that membership and non-membership degrees play a symmetric role. This methodology is of great importance in many decision-making situations. In this paper, we provide a novel theoretical approach to solve decision-making problems based on bipolar fuzzy soft sets and study four types of parameter reductions of such sets. Parameter reduction algorithms are developed and illustrated through examples. The experimental results prove that our proposed parameter reduction techniques delete the irrelevant parameters while keeping definite decision-making choices unchanged. Moreover, the reduction algorithms are compared regarding the degree of ease of computing reduction, applicability, exact degree of reduction, applied situation, and multi-use of parameter reduction. Finally, a real application is developed to describe the validity of our proposed reduction algorithms.

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

Cited by 60 publications

References 52 publications

A Consensus Measure of Expert Judgment in the Fuzzy TOPSIS Method

A Consensus Measure of Expert Judgment in the Fuzzy TOPSIS Method

Multipolar Intuitionistic Fuzzy Set with Finite Degree and Its Application in BCK/BCI-Algebras

Parameter Reductions of Bipolar Fuzzy Soft Sets with Their Decision-Making Algorithms

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