Hierarchical Decision Making Using Neutrosophic Tree Soft Sets for Multi-Criteria Evaluation

In complex decision-making scenarios involving imprecise, uncertain, and inconsistent data, classical evaluation techniques often fall short. This study presents a effective (MCDM) approach using Neutrosophic Tree Soft Set (NTSS) for the assessment and selection of optimal candidates. Three independent decision makers (DM1–DM3) use the recommended approach to assess five candidates (C1–C5) based on their performance. Several kinds of aggregation operators, such as the arithmetic operator(NA),weighted arithmetic operator(NA_w),geometric operator (NG), weighted geometric operator (NG_w), harmonic operator (NH), and weighted harmonic operator (NH_w) used efficiently aggregate the decision makers' input. To ensure a comprehensive and consistent decision outcome, classical decision-making strategies such as the Laplace criterion (equal likelihood), Optimism criterion (maximax, maximin) and Savage criterion (minimax regret) are engaged in combination with the aggregated NTSS evaluations. The integration of neutrosophic logic within tree-structured soft sets allows for more nuanced representation and manipulation of indeterminate and conflicting information. The final ranking of candidates reflects a balanced and transparent decision process, offering significant utility for human resource selection, strategic planning, and other fields where subjectivity and ambiguity prevail.