<![CDATA[This study proposes a grouped-object fuzzy N-soft framework for decision-making problems involving clustered or partitioned data. The proposed framework extends the conventional fuzzy N-soft set model by integrating the concepts of strait fuzzy sets and strait soft sets, thereby enabling the representation of objects at both the individual and group levels. Unlike existing fuzzy N-soft models, which evaluate objects individually, the proposed approach incorporates group-based partitions while preserving fuzzy membership information and grade rankings.A score-based decision-making procedure is developed to compare groups under multiple evaluation parameters. To illustrate the applicability of the framework, a case study on student performance assessment is considered, where students are grouped according to grade classes and fuzzy membership intervals. The results show that the proposed method successfully identifies the most dominant group and provides an interpretable ranking of grouped objects. The main contribution of this study is the introduction of a grouped-object extension of fuzzy N-soft sets that supports group-level decision making through strait-based partitions. This framework broadens the applicability of fuzzy N-soft sets to decision-making environments where objects naturally occur in clusters or categories. The proposed approach also establishes a foundation for future theoretical developments and practical applications involving grouped data under uncertainty.]]>
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