Interval-valued q-rung orthopair fuzzy Weber operator and its group decision-making application

TL;DR

This paper introduces a novel fuzzy decision-making method using interval-valued q-rung orthopair fuzzy sets and the Swing algorithm to evaluate learning effectiveness, demonstrating improved differentiation and accuracy.

Contribution

It develops a new MAGDM method with an extended Weber operator and optimized Swing algorithm for better evaluation of uncertain subjective data.

Findings

The proposed method effectively evaluates learning outcomes.

Swing algorithm improves ranking differentiation.

Case study confirms feasibility and effectiveness.

Abstract

The evaluation of learning effectiveness requires the integration of objective test results and analysis of uncertain subjective evaluations. Fuzzy theory methods are suitable for handling fuzzy information and uncertainty to obtain comprehensive and accurate evaluation results. In this paper, we develop a Swing-based multi-attribute group decision-making (MAGDM) method under interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs). Firstly, an extended interval-valued q rung orthopair Weber ordered weighted average (IVq-ROFWOWA) operator is introduced. Then the attribute weights deriving method is designed by using the optimized Swing algorithm. Furthermore, we develop a MAGDM method for evaluating students' learning effectiveness using the IVq-ROFWOWA operator and the Swing algorithm. Finally, a case of evaluating students' learning effectiveness is illustrated by using the proposed MAGDM method. The implementing results demonstrate that the proposed MAGDM method is feasible and effective, and the Swing algorithm enhances better differentiation in ranking alternatives compared to other methods.