Decision making using Uni-Int parameterize soft set

Abstract

Decision-making in complex environments often involves uncertainty, vagueness, and inconsistent expert opinions, which classical crisp models and traditional soft set frameworks handle inadequately. Recent studies highlight the need for hybrid parameterized structures capable of integrating interval information and user importance weights. This research aims to develop a Uni-Int parameterize soft set model for reliable multi-criteria decision making. The proposed approach defines parameters with interval valued memberships and incorporates decision maker preference indices through normalization and aggregation operators under varying parameter granularity and incomplete information conditions overall applicability A ranking algorithm based on score and accuracy functions is constructed and tested on simulated and real selection datasets covering personnel selection and supplier evaluation problems from public datasets. Comparative analysis with fuzzy, intuitionistic, and classical soft set methods evaluates consistency and computational efficiency using MATLAB based prototypes for repeatability testing and validation steps. Results show the model improves decision stability by 28% and reduces ranking conflicts across criteria weights across different decision maker priority distributions consistently. In case studies of candidate selection, the top alternative remained unchanged in 9 of 10 sensitivity scenarios indicating robustness against parameter perturbations in practice. Computation time decreased by approximately 22% compared to existing hybrid approaches while maintaining interpretability for large-scale alternatives exceeding thousand options. The Uni-Int parameterize soft set provides a flexible framework for handling interval uncertainty and subjective preferences in decision support systems. Future work will extend the model to group decision environments and real-time intelligent applications in engineering and management domains.