Abstract

Abstractq-Rung orthopair fuzzy set (qROFS) is a family of generalized fuzzy sets including intuitionistic fuzzy set, Pythagorean fuzzy set, Fermatean fuzzy set among others. q-Rung orthopair fuzzy set has higher prospect of applications in decision science because it can conveniently tackle vague problems that are beyond the reach of the aforementioned generalized fuzzy sets. The concept of composite relation is a very important information measure use to determine multiple criteria decision-making problems. This chapter proposes max-min-max composite relation under q-Rung orthopair fuzzy sets. Some theorems are used to characterize certain salient properties of q-Rung orthopair fuzzy sets. An easy to follow algorithm and flowchart of the q-Rung orthopair fuzzy max-min-max composite relation are presented to illustrate the computational processes. A case of medical decision-making (MDM) is determined in q-Rung orthopair fuzzy environment to demonstrate the applicability of the proposed q-Rung orthopair fuzzy max-min-max composite relation where diseases and patients are presented as q-Rung orthopair fuzzy values in the feature space of certain symptoms. A comparative study of intuitionistic fuzzy set, Pythagorean fuzzy set, Fermatean fuzzy set and q-Rung orthopair fuzzy set based on max-min-max composite relation is carried out to ascertain the superiority of q-Rung orthopair fuzzy set in curbing uncertainties. It is gleaned from the findings of this chapter that (i) a q-Rung orthopair fuzzy set is an advanced soft computing construct with the ability to precisely curb uncertainty compare to intuitionistic fuzzy set, Pythagorean fuzzy set and Fermatean fuzzy set, (ii) a q-Rung orthopair fuzzy max-min-max composite relation is a reliable information measure for determining decision making problems with precision.KeywordsDecision-makingIntuitionistic fuzzy setPythagorean fuzzy setq-Rung orthopair fuzzy setsq-Rung orthopair fuzzy max-min-max composite relation

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