Abstract
Orthopairs (pairs of disjoint sets) have points in common with many approaches to managing vaguness/uncertainty such as fuzzy sets, rough sets, soft sets, etc. Indeed, they are successfully employed to address partial knowledge, consensus, and borderline cases. One of the generalized versions of orthopairs is intuitionistic fuzzy sets which is a well-known theory for researchers interested in fuzzy set theory. To extend the area of application of fuzzy set theory and address more empirical situations, the limitation that the grades of membership and non-membership must be calibrated with the same power should be canceled. To this end, we dedicate this manuscript to introducing a generalized frame for orthopair fuzzy sets called "(m, n)-Fuzzy sets", which will be an efficient tool to deal with issues that require different importances for the degrees of membership and non-membership and cannot be addressed by the fuzzification tools existing in the published literature. We first establish its fundamental set of operations and investigate its abstract properties that can then be transmitted to the various models they are in connection with. Then, to rank (m, n)Fuzzy sets, we define the functions of score and accuracy, and formulate aggregation operators to be used with (m, n)-Fuzzy sets. Ultimately, we develop the successful technique "aggregation operators" to handle multi-criteria decision-making problems in the environment of (m, n)-Fuzzy sets. The proposed technique has been illustrated and analyzed via a numerical example.