Integrating Interval-Valued Complex Intuitionistic Fuzzy Hypersoft Sets in Decision-Support Systems for Optimal Product Selection

Although many refrigerator models are available in literature, the decision-support systems ([Formula: see text]) capable of handling interval-valued data and intuitionistic fuzzy hypersoft sets in complex environments remain inadequate. Existing methods often fail to meet both quantitative and qualitative needs, reducing the accuracy of selecting the best model for diverse consumer demands. Researchers face challenges such as managing uncertainty, addressing cyclical evaluations, imposing constraints, and modeling multi-parametric reasoning. To address these issues, this study proposes a novel framework for the interval-valued complex intuitionistic fuzzy hypersoft set ([Formula: see text]). The structure provides a comprehensive solution, enhancing decision-support systems for complex environments. First, the ideas of [Formula: see text] and its associated set operations are carefully examined. Several aggregation tools, including the cardinality matrix, aggregate matrix, and decision matrix, were created to address decision-support systems ([Formula: see text]). A organized process with nine distinct phases is suggested in the next step. These stages methodically move from building the necessary sets to determining the best possible solution. A prototype case study was carried out in order to verify the suggested methodology. Fourteen important assessment features and their corresponding sub-parameters were examined in this case study. The goal of the analysis was to choose the best product based on these thorough assessments. The results highlight the robustness of the technique in addressing complex decision-making scenarios effectively.