scholarly journals Multiple Attribute Decision-Making Method Using Similarity Measures of Neutrosophic Cubic Sets

Symmetry ◽  
2018 ◽  
Vol 10 (6) ◽  
pp. 215 ◽  
Author(s):  
Angyan Tu ◽  
Jun Ye ◽  
Bing Wang
Keyword(s):  
Decision Making ◽  
Similarity Measures ◽  
Multiple Attribute Decision Making ◽  
Multiple Attribute

scholarly journals Multiple-Attribute Decision-Making Method Using Similarity Measures of Hesitant Linguistic Neutrosophic Numbers Regarding Least Common Multiple Cardinality

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 330 ◽  
Author(s):  
Wenhua Cui ◽  
Jun Ye
Keyword(s):  
Decision Making ◽  
Investment Decision ◽  
Similarity Measures ◽  
Multiple Attribute Decision Making ◽  
Uncertain Information ◽  
Fuzzy Information ◽  
Common Multiple ◽  
Least Common Multiple ◽  
Multiple Attribute ◽  
Linguistic Neutrosophic Numbers

Linguistic neutrosophic numbers (LNNs) are a powerful tool for describing fuzzy information with three independent linguistic variables (LVs), which express the degrees of truth, uncertainty, and falsity, respectively. However, existing LNNs cannot depict the hesitancy of the decision-maker (DM). To solve this issue, this paper first defines a hesitant linguistic neutrosophic number (HLNN), which consists of a few LNNs regarding an evaluated object due to DMs’ hesitancy to represent their hesitant and uncertain information in the decision-making process. Then, based on the least common multiple cardinality (LCMC), we present generalized distance and similarity measures of HLNNs, and then develop a similarity measure-based multiple-attribute decision-making (MADM) method to handle the MADM problem in the HLNN setting. Finally, the feasibility of the proposed approach is verified by an investment decision case.

scholarly journals Similarity Measures of q-Rung Orthopair Fuzzy Sets Based on Cosine Function and Their Applications

Mathematics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 340 ◽  
Author(s):  
Wang ◽  
Wang ◽  
Wei ◽  
Wei
Keyword(s):  
Decision Making ◽  
Pattern Recognition ◽  
Fuzzy Sets ◽  
Similarity Measures ◽  
Multiple Attribute Decision Making ◽  
Membership Degree ◽  
Numerical Examples ◽  
Multiple Attribute ◽  
Cosine Similarity Measures ◽  
Scheme Selection

In this article, we propose another form of ten similarity measures by considering the function of membership degree, non-membership degree, and indeterminacy membership degree between the q-ROFSs on the basis of the traditional cosine similarity measures and cotangent similarity measures. Then, we utilize our presented ten similarity measures and ten weighted similarity measures between q-ROFSs to deal with multiple attribute decision-making (MADM) problems including pattern recognition and scheme selection. Finally, two numerical examples are provided to illustrate the scientific and effective of the similarity measures for pattern recognition and scheme selection.

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