A Comprehensive Analysis of Neutrosophic Bonferroni Mean Operator

A. Kanchana; D. Nagarajan; S. Broumi; Dedy Dwi Prastyo

Authors

A. Kanchana Department of Mathematics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences,Saveetha University, Chennai, India.
D. Nagarajan Department of Mathematics, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, India
S. Broumi Laboratory of Information Processing, Faculty of Science Ben M’Sik, University Hassan II, B.P 7955, Sidi Othman, Casablanca, Morocco
Dedy Dwi Prastyo Department of Statistics, Faculty of Science and Data Analytics, InstitutTeknologiSepuluhNopember, Surabaya, 60111, Indonesia

Keywords:

Group decision making in multi-attributes using Neutrosophic(NMAGDM), Neutrosophic Bonferroni operator, weighted Neutrosophic Bonferroni operator, Neutrosophic operator

Abstract

The Neutrosophic Bonferroni operator is a novel operator that we provide in this paper. Then the arithmetic operations
for Neutrosophic Bonferroni operator is developed which tells the existence of Neutrosophic Bonferroni operator.
Then its properties were discussed with special cases. To group decision-making issues with several attributes,
arithmetic ranking operations and the Neutrosophic approach are used. The result is compared with the existing
methodology. The suggested approach will more accurately give the decision maker the ideal attribute than the existing
system does. Neutrophic multicriteria is a method of decision-making that makes use of ambiguity to integrate various
criteria or factors—often with imprecise or ambiguous data—to reach a result. The neutrosophic multicriteria analysis
enables the assessment of subjective and qualitative factors, which can assist in resolving conflicting goals and
preferences. In Neutrosophic Multi-Attribute Group Decision Making (NMAGDM) problems, all the data supplied
by the decision makers (DMs) is expressed in single-value Neutrosophic triangular and trapezoidal numbers, which
are studied in this work and can improve the flexibility and precision of capturing uncertainty and aggregating
preferences. Studying this operator is crucial because it can be utilised to resolve multi-attribute

DOI: 10.5281/zenodo.10674527