Combination of the Single-Valued Neutrosophic Fuzzy Set and the Soft Set with Applications in Decision-Making

Khalil, Ahmed Mostafa; Cao, Dunqian; Azzam, A. A.; Smarandache, Florentín; Alharbi, Wedad R.

doi:10.3390/sym12081361

Cited by 22 publications

(14 citation statements)

References 25 publications

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“…"The flow chart of the algorithm decision-making based on the proposed distance measures is give in Figure 3." the generation of fuzzy sets, the SVNFS is effective at dealing with uncertainty and has been widely applied in multiattribute decision-making tasks [26][27][28].…”

Section: Normalized the Fuzzy Decision Matrix Asmentioning

confidence: 99%

Novel Distance Measures for Single-Valued Neutrosophic Fuzzy Sets and Their Applications to Multicriteria Group Decision-Making Problem

Jin

Kamran

Salamat

et al. 2022

Journal of Function Spaces

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Single-valued neutrosophic sets are a hybrid of fuzzy sets that are used to represent uncertain, imprecise, partial, and inconsistent information in the actual world. The focus of this research is to develop two novel distance measures for single-valued neutrosophic fuzzy sets (SVNFSs). We introduced two new distance measures named d η G , Y and d ζ G , Y for SVNFSs and apply these measures to different examples and also compare them with existing measures to show the validity of our proposed measures. Our results are reliable and useful for decision-making problems. We also proposed an algorithm for multicriteria group decision-making. Based on this algorithm, we find the ranking matrices using proposed distance measures. We also give an example to demonstrate the notion and concept of our algorithm.

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Section: Normalized the Fuzzy Decision Matrix Asmentioning

confidence: 99%

Novel Distance Measures for Single-Valued Neutrosophic Fuzzy Sets and Their Applications to Multicriteria Group Decision-Making Problem

Jin

Kamran

Salamat

et al. 2022

Journal of Function Spaces

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“…Future works can utilize our proposed TFNNs-MABAC method to risk assessment for MCGDM cases, such as [45][46][47][48][49][50], many other uncertain and fuzzy circumstances [51][52][53][54][55][56][57][58][59][60]. The neutrosophic sets, such as [61][62][63][64][65][66][67][68][69], can be many possible chances to improve our proposed MABAC method for further studies. Other interesting future studies are to develop a novel decision-making model based on the TFNNs in social network group decision making [70] or apply the model to two-sided matching decision-making problems [71].…”

Section: Discussionmentioning

confidence: 99%

An Extended MABAC Method Based on Triangular Fuzzy Neutrosophic Numbers for Multiple-Criteria Group Decision Making Problems

Irvanizam

Zuhra

et al. 2020

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In this manuscript, we extend the traditional multi-attributive border approximation area comparison (MABAC) method for the multiple-criteria group decision-making (MCGDM) with triangular fuzzy neutrosophic numbers (TFNNs) to propose the TFNNs-MABAC method. In the proposed method, we utilize the TFNNs to express the values of criteria for each alternative in MCGDM problems. First, we briefly acquaint the basic concept of TFNNs and describe its corresponding some operation laws, the functions of score and accuracy, and the normalized hamming distance. We then review two aggregation operators of TFNNs. Afterward, we combine the traditional MABAC method with the triangular fuzzy neutrosophic evaluation and provide a sequence of calculation procedures of the TFNNs-MABAC method. After comparing it with some TFNNs aggregation operators and another method, the results showed that our extended MABAC method can not only effectively handle the conflicting attributes, but also practically deal with incomplete and indeterminate information in the MCGDM problem. Therefore, the extended MABAC method is more effective, conformable, and reasonable. Finally, an investment selection problem is demonstrated as a practice to verify the reasonability of our MABAC method.

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“…In future research, we plan to further investigate along with the following: (1) topological properties of the presented methods [44,45], (2) combination with the soft set and the proposed methods [46,47], and (3) combination with the neutrosophic set and the current methods [48].…”

Section: Discussionmentioning

confidence: 99%

Certain Types of Covering-Based Multigranulation ( ℐ , T )-Fuzzy Rough Sets with Application to Decision-Making

et al. 2020

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As a generalization of Zhan’s method (i.e., to increase the lower approximation and decrease the upper approximation), the present paper aims to define the family of complementary fuzzy β -neighborhoods and thus three kinds of covering-based multigranulation ( ℐ , T )-fuzzy rough sets models are established. Their axiomatic properties are investigated. Also, six kinds of covering-based variable precision multigranulation ( ℐ , T )-fuzzy rough sets are defined and some of their properties are studied. Furthermore, the relationships among our given types are discussed. Finally, a decision-making algorithm is presented based on the proposed operations and illustrates with a numerical example to describe its performance.

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Combination of the Single-Valued Neutrosophic Fuzzy Set and the Soft Set with Applications in Decision-Making

Cited by 22 publications

References 25 publications

Novel Distance Measures for Single-Valued Neutrosophic Fuzzy Sets and Their Applications to Multicriteria Group Decision-Making Problem

Novel Distance Measures for Single-Valued Neutrosophic Fuzzy Sets and Their Applications to Multicriteria Group Decision-Making Problem

An Extended MABAC Method Based on Triangular Fuzzy Neutrosophic Numbers for Multiple-Criteria Group Decision Making Problems

Certain Types of Covering-Based Multigranulation ( ℐ , T )-Fuzzy Rough Sets with Application to Decision-Making

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