A Hybridization of Modified Rough Bipolar Soft Sets and TOPSIS for MCGDM

Uncertain data is a challenge to decision-making (DM) problems. Multi-criteria group (MCGDM) problems are among these that have received much attention. MCGDM difficult because the existing alternatives frequently conflict with each other. In this article, we suggest novel hybrid model for an approach based on modified rough bipolar soft sets (MRBSs) using well-known method of technique order preference by similarity ideal solution (TOPSIS), which combines MRBSs theory and TOPSIS prioritization in uncertain environment. technique, first introduce aggregated parameter matrix help lower upper matrices identify positive negative solutions. After that, define separation measurements two solutions compute relative closeness choose best alternative. Next, application proposed problem introduced. Afterward, algorithm developed, illustrated case study. The demonstrates usefulness efficiency proposal. Compared some studies, additionally present several merits our technique. Eventually, paper handles whether additional studies topics needed.