Multi-attribute decision-making by using intuitionistic Fuzzy rough Aczel-Alsina prioritize Aggregation Operator

Abstract

The management of uncertain and symmetric information is a difficult task. To sort out this issue, multi-attribute decision-making (MADM) is a crucial methodology in decision-making (DM) sciences in which we can select the most suitable and reasonable alternative from uncertain information. Furthermore, this article proposes prioritized operators by allowing the positive real value known as priority degree among the strict priority levels. In addition, Aczel-Alsina (AA) t-norm (TNM) (AATNM) and AA t-conorm (TCNM) (AATCNM) are newly proposed concepts in fuzzy set (FS) theory. However, when experts use AATNM and AATCNM operation for rough FS (RFS), these operational rules fail to aggregate information because it has lower and upper approximations spaces. Thus, an encasement of an intuitionistic FS (IFS) has many chances to lose information. Still, on the other hand, the intuitionistic fuzzy rough (IFR) set (IFRS) has resolved the data loss issue. By taking advantage of IFRS structure, priority degree, and AATNM, AATCNM, we constructed a new operation for IFR values (IFRVs). Also, proposed new aggregation operators (AOs) called IFR Aczel-Alsina prioritized weighted averaging (IFRAAPWA) and IFR Aczel-Alsina prioritized weighted geometric (IFRAAPWG) operators. Some necessary axioms of proposed AOs are also discussed. The MADM approach is developed based on the proposed AOs and utilized in medical diagnosis (MD) problems. The characteristics of the proposed technique are also compared with many present methodologies, which highlight the importance and superiority of suggested work over the prevailing approaches. The effect of parameters on aggregation findings is also investigated, which plays a vital role in outcomes.