A MAGDM approach based on dual hesitant Q-rung orthopair fuzzy Dombi norm with Hamy mean operators and its application.

As complex real-world decision-making intensifies, handling inherent fuzziness and uncertainty becomes crucial. Dual hesitant q-rung orthopair fuzzy (DHq-ROF) sets offer a solution for addressing ambiguity and hesitancy for multi-attribute group decision-making (MAGDM). However, the existing MAGDM methods fail to simultaneously consider attribute correlation and flexibility, and involve lower variation rate of the scored values. Additionally, these techniques concern distance measurement with normalization, which leads to information redundancy. To address these above issues, we propose a new MAGDM approach based on DHq-ROF Dombi norm with Hamy Mean (HM) Operators. First, the HM operator considering attribute correlation and the Dombi t-norms and t-conorms (Dt-N&t-CNs) which offer enhanced flexibility are combined within the DHq-ROF environment, proposing DHq-ROF weighted Dombi HM and DHq-ROF weighted Dombi dual HM aggregation operators to obtain comprehensive evaluative information. Second, a new distance measurement method without normalization is put forward to avoid information redundancy, and subsequently, the related entropy measures for objectively deriving the undetermined weights of attributes are presented. Third, this study proposes a novel DHq-ROF-based approach for MAGDM. Finally, a case study on traditional Chinese medicine-based sleep disorder diagnosis confirms the method’s effectiveness, achieving a higher variation rate of the scored values while simultaneously considering flexibility and attribute correlation.