Intuitionistic Fuzzy Rough TOPSIS Method for Robot Selection using Einstein operators

Abstract Rough set and intuitionistic fuzzy set are very vital role in the decision making method for handling the uncertain and imprecise data of decision makers. The technique for order preference by similarity to ideal solution (TOPSIS) is very attractive method for solving the ranking and multi-criteria decision making (MCDM) problem. The primary goal of this paper is to introduce the Extended TOPSIS for industrial robot selection under intuitionistic fuzzy rough (IFR) information, where the weights of both, decision makers (DMs) and criteria are not-known. First, we develop Intuitionistic fuzzy rough (IFR) aggregation operators based on Einstein T-norm and T-conom, For this firstly we give the idea of intuitionistic fuzzy rough Einstein weighted averaging (IFREWA), intuitionistic fuzzy rough Einstein hybrid averaging (IFREHA) and intuitionistic fuzzy rough ordered weighted averaging (IFREOWA) aggregation operators. The fundamental properties of the proposed operators are described in detail. Furthermore to determine the unknown weights, a generalized distance measure are defined for IFRSs based on intuitionistic fuzzy rough entropy measure. Following that, the intuitionistic fuzzy rough information-based decision-making technique for multi-criteria group decision making (MCGDM) is developed, with all computing steps depicted in simplest form. For considering the conflicting attributes, our proposed model is more accurate and effective. Finally, an example of efficient industrial robot selection is presented to illustrate the feasibility of the proposed intuitionistic fuzzy rough decision support approaches, as well as a discussion of comparative outcomes, demonstrating that the results are feasible and reliable.