A novel ( p , q )-rung orthopair fuzzy Sugeno–Weber prioritized operator framework with an improved score function for IoT platform assessment under uncertainty

IoT platforms drive digital transformation in engineering management, but their evaluation is challenging due to uncertainty and incompleteness. To address these issues, this study employs (p,q)-rung orthopair fuzzy sets, which provide an effective mechanism for handling uncertainty. The research introduces prioritized aggregation operators that integrate conflicting or redundant information according to the relative importance of evaluation criteria. By extending Sugeno–Weber triangular norms to the (p,q)-rung orthopair fuzzy context, the study develops multiple operators, including average, weighted average, geometric, and weighted geometric, that ensure accurate, stable, and flexible aggregation of assessments. Recognizing that existing (p,q)-rung orthopair fuzzy score functions often neglect hesitation and fail to distinguish alternatives effectively, a novel score function is proposed to enhance differentiation. Applying the framework to ten IoT platforms, R1 (Huawei) is identified as the top-ranked platform (score: 0.59414), whereas R10 is ranked lowest (score: 0.22954). Robustness is confirmed through sensitivity, comparative, statistical, and scalability analyzes.