Comparative analysis of multi-criteria decision-making methods using q-rung orthopair fuzzy set: evaluating the impact of distance measures and aggregation operators

Purpose The increasing complexity and uncertainty in real-world decision-making necessitate advanced modeling tools capable of capturing complex human judgments. This study presents a comprehensive comparative analysis of multi-criteria decision-making (MCDM) methods within the framework of q-rung orthopair fuzzy sets (q-ROFS), emphasizing the influence of distance measures and aggregation operators on ranking stability. Design/methodology/approach Three widely adopted MCDM techniques, TOPSIS, CODAS, and VIKOR, are implemented using multiple distance metrics and aggregation strategies to evaluate their sensitivity and consistency. Findings The results reveal that TOPSIS and CODAS maintain stable ranking patterns across different distance measures, while VIKOR demonstrates greater sensitivity, leading to rank fluctuations. Additionally, the selection of aggregation operators significantly affects final decision outcomes, underscoring their critical role in the MCDM process. Practical implications A real-world case study validates the proposed framework, illustrating its practical applicability and highlighting key methodological insights. Originality/value This work contributes to the literature by systematically exploring the interplay between distance measures, aggregation mechanisms, and MCDM techniques in the q-ROFS environment, offering robust guidelines for more reliable and interpretable decision analysis.