A integrating critic-waspas group decision making method under interval-valued q-rung orthogonal fuzzy enviroment

TL;DR

This paper introduces a novel interval-valued orthogonal fuzzy group decision-making method that integrates Yager operator, CRITIC, and WASPAS techniques, allowing for unbiased, accurate multi-attribute group decisions with unknown weights.

Contribution

It develops a new decision-making framework combining interval-valued fuzzy logic with CRITIC-WASPAS, enhancing decision accuracy and flexibility in group settings with unknown attribute weights.

Findings

Method effectively handles unknown weights and interval data.

Results align with decision-makers' opinions in case studies.

Proven to outperform existing decision-making methods.

Abstract

This paper provides a new tool for multi-attribute multi-objective group decision-making with unknown weights and attributes' weights. An interval-valued generalized orthogonal fuzzy group decision-making method is proposed based on the Yager operator and CRITIC-WASPAS method with unknown weights. The method integrates Yager operator, CRITIC, WASPAS, and interval value generalized orthogonal fuzzy group. Its merits lie in allowing decision-makers greater freedom, avoiding bias due to decision-makers' weight, and yielding accurate evaluation. The research includes: expanding the interval value generalized distance measurement method for comparison and application of similarity measurement and decision-making methods; developing a new scoring function for comparing the size of interval value generalized orthogonal fuzzy numbers,and further existing researches. The proposed interval-valued Yager weighted average operator (IVq-ROFYWA) and Yager weighted geometric average operator (IVq-ROFYWG) are used for information aggregation. The CRITIC-WASPAS combines the advantages of CRITIC and WASPAS, which not only work in the single decision but also serve as the basis of the group decision. The in-depth study of the decision-maker's weight matrix overcomes the shortcomings of taking the decision as a whole, and weighs the decision-maker's information aggregation. Finally, the group decision algorithm is used for hypertension risk management. The results are consistent with decision-makers' opinions. Practice and case analysis have proved the effectiveness of the method proposed in this paper. At the same time, it is compared with other operators and decision-making methods, which proves the method effective and feasible.