Dynamic Fermatean Fuzzy Set

Multicriteria decision-making (MCDM) involves the selection and prioritization of alternatives based on multiple, often conflicting, criteria. Traditional MCDM methods typically rely on the assumption of static data, ignoring the dynamic nature of real-world environments. To address this limitation, this chapter introduces the Dynamic Fermatean Fuzzy Set (DFFS), a novel temporal extension of Fermatean fuzzy theory that enables more expressive modeling of belief and opposition over time. A DFFS-TOPSIS method is proposed, integrating the newly defined DFFS into the Classical Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) framework to support dynamic, fuzzy decision-making. A case study involving tutor performance evaluation over multiple periods was conducted to validate the proposed DFFS-TOPSIS method. The results demonstrate the method&s;s ability to effectively capture temporally varying expert evaluations and provide robust and reliable rankings. The DFFS-TOPSIS approach makes a valuable contribution to dynamic MCDM by enhancing flexibility, accommodating temporal uncertainty, and supporting informed decision-making in evolving environments.