Interval-Valued Pythagorean Fuzzy Information Aggregation Based on Aczel-Alsina Operations and Their Application in Multiple Attribute Decision Making

Abstract

The multi-attribute decision-making (MADM) has really become widely utilized in many domains, including management, economics, and other disciplines. It plays a significant role in mainstream decision science. Its main process is the ranking of choices available and selecting the optimal option out of a group of options based on a set of attribute values. Aggregation operators (AOs) play a significant role in MADM techniques to aggregate uncertain and vague information. Aczel Alsina AOs are recently introduced to handle with ambiguous or uncertain information and has their significance. This article aims to generalize the concept “Aczel Alsina AOs of Pythagorean fuzzy sets” (PyFSs) to interval-valued Pythagorean fuzzy (IVPyF) information by utilizing the Aczel-Alsina t-norm (AA-TNM) and t-conorm (AA-TCNM). We developed a series of Aczel-Alsina AOs such as “IVPyF Aczel-Alsina weighted averaging” (IVPyFAAWA), “IVPyF Aczel-Alsina ordered weighted averaging” (IVPyFAAOWA), and “IVPyF Aczel-Alsina hybrid weighted averaging” (IVPyFAAHWA) operators. We also studied the “IVPyF Aczel-Alsina weighted geometric” (IVPyFAAWG) operator. We interpreted certain characteristics of our invented approaches. To check the effectiveness and reliability of our proposed approaches, we establish an illustrative numerical example for the selection of research scientist. The sensitivity analysis of the parameters is also established and their impact on ranking results is analyzed. We illustrated a realistic comparison in which we compare outcomes of exiting methodologies with the consequences of current discussed approaches.