An effective correlation-based compromise approach for multiple criteria decision analysis with Pythagorean fuzzy information

The aim of this paper is to develop a novel correlation-based compromise approach for addressing multiple criteria decision analysis problems under complex uncertainty based on Pythagorean fuzzy sets. Based on the concepts of information energy and correlations for Pythagorean fuzzy characteristics, this paper proposes useful correlation-based compromise indices and investigates their desirable properties. These compromise indices can be employed to underlie anchored judgments and to reflect a certain balance between the correlations with positive-ideal and negative-ideal points of reference. Moreover, they can fully take into consideration the amount of information associated with higher degrees of uncertainty and can effectively fuse subjective assessments conveyed by Pythagorean fuzziness. From two different perspectives of displaced and fixed ideals, this paper provides two algorithmic procedures of the proposed correlation-based compromise approach for conducting multiple criteria evaluation tasks within Pythagorean fuzzy environments. A real-world problem concerning a financing decision on working capital policies is investigated to show the feasibility and applicability of the developed techniques. The application results, along with a comparative analysis, demonstrate the practicality and effectiveness of the proposed methodology.