<p>Dual hesitant q-rung orthopair fuzzy (DHq-ROF) numbers exhibit superior performance in characterizing complex uncertain and ambiguous information compared to q-rung orthopair fuzzy (q-ROF) numbers and dual hesitant fuzzy (DHF) numbers. Nevertheless, existing studies on DHq-ROF-based MADM still have some research gaps that require resolution: first, most existing entropy measures for DHq-ROF numbers suffer from inadequate discrimination capability and low computational efficiency, failing to accurately quantify the uncertainty inherent in DHq-ROF information; second, conventional similarity measures for DHq-ROF numbers do not incorporate the influence of entropy (i.e., the degree of uncertainty), leading to imprecise characterization of the similarity between DHq-ROF numbers; third, the traditional multiplicative multi-objective optimization approach by ratio analysis (MULTIMOORA) method has not been effectively extended to the DHq-ROF context, and weight-determining methods in existing DHq-ROF-based MADM methods typically neglect the integration of subjective and objective preferences, potentially resulting in biased decision outcomes. To bridge these research gaps and effectively tackle MADM problems evaluated by DHq-ROF numbers, this study proposes a novel MADM method, namely the DHq-ROF-MULTIMOORA method. Specifically, a new entropy measure for DHq-ROF numbers is initially developed to accurately characterize the uncertainty of DHq-ROF information, which outperforms existing entropy measures in discrimination capability and computational efficiency. Subsequently, considering the influence of the proposed entropy measure, a novel cosine similarity measure between two DHq-ROF numbers is constructed to precisely depict their similarity relationship, and its advantages and applicability are validated through comparisons with existing similarity measures. Based on the proposed entropy and cosine similarity measures, the DHq-ROF-MULTIMOORA method is established by extending the traditional MULTIMOORA to the DHq-ROF environment. In detail, a novel weight-determining method is designed to integrate both subjective and objective weight assignment approaches, ensuring the rationality and reliability of attribute weights. A practical case study is conducted to demonstrate the practicality and effectiveness of the proposed DHq-ROF-MULTIMOORA method. Finally, sensitivity and comparison analysis are performed, and the results confirm the flexibility, effectiveness, and superiority of the proposed method in solving DHq-ROF-based MADM problems.</p>