Multiple Attribute Decision-Making Based on Bonferroni Mean Operators under Square Root Fuzzy Set Environment

Abstract

The intuitionistic fuzzy set (IFS), which has a membership and non-membership degree, is a controlling and effective device for dealing
with fuzziness and uncertainty. Recently,the square root fuzzy set which is one of the efficient generalizations of an IFS for dealing with uncertainty and haziness in information has beenintroduced. In this study, a novel method for multiple attribute decision-making (MADM) based on SR-fuzzy information isinvestigated. Since aggregation operators are significant in the decision-making (DM) process, to achievethis goal, the current paper suggests a variety of novel Bonferroni mean and weighted Bonferroni mean operators to aggregate the SR-fuzzy values for the various decision-maker preferences. To achieve this goal, the current paper suggests a variety of novel Bonferroni mean and weighted Bonferroni mean operators to aggregate the SR-fuzzy values for the various decision-maker preferences. SR-fuzzyBonferroni mean operator and weighted SR-fuzzy Bonferroni mean operator are established and their properties are described. Then, we constructed a MADM approach using the proposed operators for the SR-fuzzyinformation and proved the approach with a mathematical example. Inthe end, a comparative study ofthe developed and existing approaches has been discussed to evaluate the pertinency and practicality of the proposed DM technique.

Received: 24 August 2022 | Revised: 21 October 2022 | Accepted: 1 November 2022

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.