Аңдатпа
Multi-attribute decision-making (MADM) problems often involve uncertain, bipolar, and complex information that cannot be adequately represented by conventional fuzzy set models. To address this limitation, this study introduces the Circular Bipolar Complex Neutrosophic Fuzzy Set (CBCNFS) framework and develops two aggregation operators: the Circular Bipolar Complex Neutrosophic Fuzzy Frank Power Averaging (CBCNFFPA) operator and the Circular Bipolar Complex Neutrosophic Fuzzy Frank Power Geometric (CBCNFFPG) operator. The proposed operators enable the aggregation of multidimensional uncertain information and satisfy fundamental properties, including idempotency, boundedness, and monotonicity. Based on these operators, a score-function-based MADM algorithm is developed to evaluate and rank alternatives. A numerical example involving hypothetical electric vehicle charging stations illustrates the application of the proposed framework to information characterised by bipolarity, complex-valued representations, neutrosophic uncertainty, and circular structures. Sensitivity analyses are conducted to examine the effects of variations in the Frank parameter, criterion weights, and CBCNF evaluations on the resulting rankings. The results illustrate the applicability of the proposed aggregation operators and their responses to changes in the considered parameters and evaluation conditions. The proposed framework provides an integrated approach to representing and aggregating multidimensional uncertain information in MADM problems.