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

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 TFN...

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Bibliographic Details
Published in:Axioms 2020-09, Vol.9 (3), p.104
Main Authors: Irvanizam, Irvanizam, Zi, Nawar Nabila, Zuhra, Rahma, Amrusi, Amrusi, Sofyan, Hizir
Format: Article
Language:English
Subjects:
Accuracy
Agglomeration
Approximation
Case studies
Decision making
Decision theory
Fuzzy sets
invested technology enterprise
Linguistics
MABAC method
Mathematical analysis
MCGDM problems
Multiple criterion
Operators
TFNNs-MABAC method
triangular fuzzy neutrosophic sets (TFNSs)
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Summary: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.
ISSN:2075-1680
2075-1680
DOI:10.3390/axioms9030104