A novel hesitant fuzzy tensor-based group decision-making approach with application to heterogeneous wireless network evaluation.

In decision environments characterized by vagueness and uncertainty, traditional models often struggle to accommodate the inherent hesitation in expert judgments. To address this challenge, this study introduces the novel concept of the Hesitant Fuzzy Tensor (HFT), a multidimensional extension of hesitant fuzzy sets, capable of representing multiple opinions across complex criteria spaces. The proposed HFT framework captures hesitation more effectively by organizing hesitant fuzzy data within tensorial structures, enabling more comprehensive analysis in group decision-making scenarios. Theoretical foundations of HFT are rigorously developed, including formal definitions, fundamental operations, and algebraic properties. Several theorems are presented to establish the mathematical consistency and operational soundness of the structure. Furthermore, a group decision-making algorithm tailored for HFT is formulated, leveraging aggregation operators and a scoring mechanism that accounts for maximum hesitation values. To demonstrate the practical utility of the proposed framework, an application to the selection of optimal heterogeneous wireless communication networks is conducted. Multiple wireless technologies are evaluated under various performance criteria using expert assessments expressed in hesitant fuzzy form. The results highlight the robustness, interpretability, and effectiveness of the HFT-based approach in complex real-world decision problems. This research not only advances the theoretical landscape of hesitant fuzzy modeling but also provides a scalable and realistic tool for uncertainty-based decision analysis in emerging technological domains.