Hesitant fuzzy N-soft sets: A new model with applications in decision-making

Akram, Muhammad; Adeel, Arooj; Alcantud, José Carlos R.

doi:10.3233/jifs-181972

Cited by 80 publications

(43 citation statements)

References 60 publications

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“…Hesitant fuzzy computations make the decision-makers' assessments more flexible and rich, thus improving reliability of the decisions that depend on them. Akram-Arooja-Carlos [110] introduced a novel hybrid model called hesitant fuzzy N-soft sets, which further enhances the virtues of hesitant fuzzy set theory with the benefits of N-soft sets. This theoretical model is capable of incorporating information about the occurrence of ratings or grades in a hesitant environment.…”

Section: Bvstsaimentioning

confidence: 99%

Applications of Soft Set in Decision Making Problems

Sai¹

2020

Global Journal of Pure and Applied Mathematics

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Section: Bvstsaimentioning

confidence: 99%

Applications of Soft Set in Decision Making Problems

Sai¹

2020

Global Journal of Pure and Applied Mathematics

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“…Step 3: Use Equations (15) and (17) to determine the geometric distancesd(A i , A + ) andd(A i , A − ) for the alternative A i (i = 1, 2, . .…”

Section: Geometric Distancementioning

confidence: 99%

“…Wang and Li [12] developed the generalized prioritized weighted average operator, the generalized prioritized weighted geometric operator with SVNHFS, and further developed an approach on the basis of the proposed operators to solve MADM problems. Recently, Akram et al [13][14][15][16] and Naz et al [17][18][19] put forward certain novel decision-making techniques in the frame work of extended fuzzy set theory. Furthermore, Liu and Shi [20] proposed the concept of INHFS by combining INS with HFS and developed the generalized weighted operator, generalized ordered weighted operator, and generalized hybrid weighted operator with the proposed interval neutrosophic hesitant fuzzy information.…”

Section: Introductionmentioning

confidence: 99%

Generalization of Maximizing Deviation and TOPSIS Method for MADM in Simplified Neutrosophic Hesitant Fuzzy Environment

2019

Self Cite

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With the development of the social economy and enlarged volume of information, the application of multiple-attribute decision-making (MADM) has become increasingly complex, uncertain, and obscure. As a further generalization of hesitant fuzzy set (HFS), simplified neutrosophic hesitant fuzzy set (SNHFS) is an efficient tool to process the vague information and contains the ideas of a single-valued neutrosophic hesitant fuzzy set (SVNHFS) and an interval neutrosophic hesitant fuzzy set (INHFS). In this paper, we propose a decision-making approach based on the maximizing deviation method and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to solve the MADM problems, in which the attribute weight information is incomplete, and the decision information is expressed in simplified neutrosophic hesitant fuzzy elements. Firstly, we inaugurate an optimization model on the basis of maximizing deviation method, which is useful to determine the attribute weights. Secondly, using the idea of the TOPSIS, we determine the relative closeness coefficient of each alternative and based on which we rank the considered alternatives to select the optimal one(s). Finally, we use a numerical example to show the detailed implementation procedure and effectiveness of our method in solving MADM problems under simplified neutrosophic hesitant fuzzy environment.

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“…In the case of many complex, real decision problems solved with the participation of a group of decision makers (experts), it is important to capture the uncertainty of opinions and preferences expressed [8]. In such situations, many of the available modifications of the TOPSIS method can be used [6,9] with various forms of data representation, for example fuzzy numbers (FN) [10][11][12][13], intuitionistic fuzzy sets (IFS) [14,15], hesitant fuzzy sets (HFS) [16,17], hesitant fuzzy N-soft sets [18], dual extended hesitant fuzzy sets (DEHFS) [19], probabilistic soft sets (PSS) [20], ordered fuzzy numbers (OFNs) [21,22], and interval data [23,24].…”

Section: Introductionmentioning

confidence: 99%

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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Hesitant fuzzy N-soft sets: A new model with applications in decision-making

Cited by 80 publications

References 60 publications

Applications of Soft Set in Decision Making Problems

Applications of Soft Set in Decision Making Problems

Generalization of Maximizing Deviation and TOPSIS Method for MADM in Simplified Neutrosophic Hesitant Fuzzy Environment

A Consensus Measure of Expert Judgment in the Fuzzy TOPSIS Method

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