A signed distance based ranking approach with unknown fuzzy priority vectors for medical diagnosis involving interval type-2 trapezoidal pythagorean fuzzy preference relations

In many of our real life problems, we often come across situations where there is no information about the priority weights which make it difficult to analyze the objects under consideration. Instead of employing simple fuzzy sets, “interval type-2 trapezoidal pythagorean fuzzy preference relations (IT2TrPFPRs)” can be used which have better representational power and ability to cope with uncertain situations. The approach discussed in this article is an effective tool for managing multiple criteria group decision-making situations with completely unknown priority weights modeled as IT2TrPFPRs. To aggregate the opinion of multiple decision-makers, a hybrid averaging operation based on weighted averaging and ordered weighted averaging (OWA) operations is employed for a collective decision environment. To calculate the fuzzy priority weight vectors in case of completely unknown environment, we construct a non-linear optimization model. An integrated optimization model based on a new signed distance-based closeness coefficients approach is employed to determine the priority ranking of alternatives. Feasibility of the proposed technique is discussed with an implementation of patient centered medicine system for choosing the appropriate treatment method. Moreover, a comparative investigation with previous approaches is conducted to demonstrate the effectiveness of the given approach.