Fuzzy soft tensor based group decision making approach with application to heterogeneous wireless network evaluation

This study introduces a novel group decision-making framework based on the Fuzzy Soft Tensor (FST) model to effectively address complex multi-criteria decision-making (MCDM) problems under uncertainty and imprecise expert judgments. The proposed FST structure integrates the strengths of fuzzy set theory and soft set theory within a multidimensional tensorial framework, offering a powerful and flexible approach to modeling expert knowledge across alternatives, criteria, and decision-makers. A new aggregation-driven group decision-making algorithm is developed to systematically combine diverse expert evaluations and ensure consistent ranking of alternatives. To demonstrate the applicability and robustness of the proposed FST-based framework, a real-world case study on heterogeneous wireless network selection is presented. Six competing technologies are evaluated against six critical performance criteria. The experimental results indicate that the FST-based approach identifies 5G NR as the most suitable network alternative, showing strong agreement with established MCDM methods such as TOPSIS, GRA, MOORA, and WASPAS. Comparative analysis further highlights that the FST model improves the handling of vague, inconsistent, and multi-perspective data while maintaining computational efficiency and interpretability. These findings confirm the scalability and reliability of the FST framework as an effective decision-support tool for complex and dynamic environments.