HyperRough TOPSIS Method and SuperHyperRough TOPSISMethod

Abstract

Rough set theory provides a mathematical framework for approximating subsets through lower and upper
bounds defined by equivalence relations, effectively capturing uncertainty in classification and data analysis.
Building on these foundational ideas, extended models such as Hyperrough Sets and Superhyperrough
Sets have been proposed to represent more complex forms of uncertainty.
In this paper, we introduce the HyperRough TOPSIS Method and the SuperHyperRough TOPSIS
Method, and examine their underlying mathematical structures. TOPSIS is a well-established method
in decision-making, and the proposed HyperRough and SuperHyperRough TOPSIS methods serve as
generalized extensions of the classical Rough TOPSIS approach.