This paper presents a novel decision-making approach based on the complex (n,m)th power root fuzzy set, which provides a powerful framework for managing two-dimensional uncertainty in evaluation problems. Existing models often struggle to offer the flexibility and robustness needed to handle diverse and uncertain real-world scenarios. To address this gap, we introduce two innovative aggregation operators–the (n,m)th power root fuzzy Dombi-weighted average operator and the weighted geometric operator–developed using flexible Dombi operations. These operators incorporate adjustable parameters, allowing for more precise control of uncertainty representation, and are formulated through rigorous mathematical principles, including sum, product, scalar multiplication, and power. Their theoretical properties are thoroughly analyzed to confirm logical consistency and reliability. A practical application involving university selection illustrates the effectiveness of the proposed method, where one alternative consistently emerges as the top choice across various parameter settings. Compared to existing methods, the new operators show significantly improved ranking stability, consistency, and robustness. Furthermore, sensitivity analysis and visual evaluation reveal how parameter changes influence decision outcomes, adding interpretive depth to the model. This study not only contributes new mathematical tools to the field of fuzzy decision-making but also offers a flexible and reliable solution for complex multi-attribute decision problems.
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