Modal multi-attribute group decision making with unknown weights and new scores under interval-valued q-rung orthopair fuzzy sets and its applications

Abstract

The way addresses multi-attribute group decision-making (MAGDM) under interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs), which integrates information from various dimensions while considering both the weights and correlations of multiple factors. By introducing IVq-ROFSs, the decision-making process can more effectively handle the uncertainty of fuzzy factors involved in decision-making. A critical aspect of the model is the need to evaluate expert contributions and the impact of attribute weights on the quality of decision outcomes. Additionally, the ranking of fuzzy numbers is a key component in the process. Our key contributions are as follows: Firstly, Expert Weighting Method is proposed to improve the accuracy of expert weighting by considering the varying influence of experts in different contexts. Secondly, it is developed that a graph-structured adaptive attribute weighting approach considers the selection of attributes by alternatives, their supporting degrees, and the relationship between them. Furthermore, a novel scoring function is introduced, overcoming the limitations of existing functions, particularly in terms of effectively comparing magnitudes. Finally, a modal multi-attribute group decision-making model, which demonstrates good generalizability on both open-source and private datasets and offers extensibility for integrating various operators, is introduced. This framework enhances decision-making accuracy and adaptability in complex, fuzzy environments, providing valuable insights for a wide range of decision-making applications.