A New 2-Tuple Linguistic Approach for Unbalanced Linguistic Term Sets

Several real-world problems employ linguistic-based approaches to handle qualitative data. The set of linguistic terms that is utilized in the problems are mostly alleged to be symmetrically distributed. However, with the advent of time, as the complexity of the problem increases, the equidistant linguistic term set seems improper. Consequently, in such cases, experts often prefer to use the set of the unbalanced linguistic term to direct the appraisal for the problems. In this article, we tend to propose a method that is newly designed to deal with a set of unbalanced linguistic terms. In this direction, we initially propose an algorithm to represent unbalanced linguistic information via a multiplicative linguistic label set that has a global inconsistent linguistic term distribution. Furthermore, in light of the Herrera and Martinez, "2-tuple linguistic model," we develop a novel 2-tuple approach for the unbalanced linguistic set, which is based on the notion of minimum distance measure. Finally, to validate the proposed model in the physical realm and to demonstrate the functioning of the method, a numerical example is being elucidated. The proposed methodology seeks to indicate a reduction in the computation time and also enhances the decision-makers' evaluations.