A Relational Approach Based on Near Intuitionistic Fuzzy Soft Sets for Multi-Criteria Decision Making

Decision-making tasks in practical environments frequently involve incomplete or ambiguous information. Intuitionistic fuzzy soft sets offer a flexible way to represent such data through membership and non-membership degrees. Nevertheless, many existing intuitionistic fuzzy soft set based multi-criteria decision-making methods mainly depend on aggregation mechanisms and often ignore the relational interactions among alternatives. To overcome this limitation, this study introduces near intuitionistic fuzzy soft (NIFS) sets within the setting of nearness approximation spaces and develops a relational structure called the near intuitionistic fuzzy soft relation (NIFSR). Based on this framework, a relation-oriented decision-making method is constructed in which relational composition is employed to incorporate both direct evaluations and interdependence among alternatives.The proposed approach is evaluated using three benchmark problems related to agricultural land assessment, sustainable supplier selection, and dengue fever diagnosis. Experimental results demonstrate that the method generates consistent and interpretable rankings under different parameter values and produces outcomes comparable with several established decision-making techniques.