Development of Intelligent based Fuzzy Decision-Making Model Through ff'-Fractional Fuzzy Information

Abstract

Making reliable and precise assessments is challenging in the context of fuzzy sets (FSs) due to hesitancy and uncertainty in non-membership degree (NMD). Researchers have been developing methods to reduce these issues, which has encouraged the creation of novel decision support tools. Therefore, in this paper, we introduce a new structure called ff′′-fractional fuzzy sets (ff′′-FFSs). The proposed structure is the extension of p, q-rung orthopair fuzzy sets and fractional fuzzy sets, where and are different fractional parameters. This provides an extra space for the experts to convey their information more accurately. Following that, we define a series of arithmetic and weighted aggregation operators to aggregate expert information into combined assessment information. Next, we propose a new method called ff′-fractional fuzzy combinative distance-based assessment ff′-FF CODAS method. This method assesses alternatives using Euclidean and taxicab distances, which enhances its ability to distinguish strongly evaluated or opposing alternatives, resulting in more consistent, accurate rankings. With the addition of ff′-FF information, this method becomes more reliable, precise, and superior, enhancing assessment accuracy. After that, the proposed method is used for the selection of the best supervisor for higher studies, which is a complex decision-making problem. Thus for this, we collect the information from three experts, and hence the result is calculated. Furthermore, we conduct a comparative study to check the success and ability of the proposed method while comparing it with existing methods and operators. Lastly, a sensitivity analysis is conducted based on two parameters, f and f′, to assess the stability of the proposed method. The results of the comparison and sensitivity demonstrate that the proposed method is effective for a wide range of decision-making problems.