Decision-Making Under Deep Uncertainty: A Generalized Neutrosophic  Fuzzy Assignment Model with Multi-Criteria Multi-Expert Evaluation

Priyanka U; Dr. V. Kamal Nasir

Authors

Priyanka U Research Scholar, Department of Mathematics, The New College, Chennai, Assistant Professor, Department of Mathematics, Agurchand Manmull Jain College, Chennai.;
Dr. V. Kamal Nasir Associate Professor, Department of Mathematics, The New College, Chennai;

Keywords:

Generalized Neutrosophic Number, Multi-Criteria Decision-Making, Multi-Expert Evaluation, Priority-Optimized Mean, Assignment Problem, Deep Uncertainty, Neutrosophic Optimization

Abstract

Decision-making in complex environments often involves ambiguity,
inconsistency, and incomplete information—conditions inadequately addressed by classical fuzzy or
even interval-valued neutrosophic systems. Building upon the earlier foundations established by
Kamal Nasir and Priyanka (2025) in their works on the Multi-Expert, Multi-Criteria Neutrosophic
Fuzzy Assignment Problem and its Interval-Valued extension, this study introduces a Generalized
Neutrosophic Fuzzy Assignment Model (GNFAM) designed to operate effectively under deep
uncertainty.The proposed framework extends the neutrosophic structure to generalized
neutrosophic numbers, enabling the representation of varying degrees of truth, indeterminacy, and
falsity in flexible forms (single-valued, interval-valued, or polygonal). A multi-expert, multi-criteria
evaluation mechanism is integrated through a Priority-Optimized Aggregation Mean, allowing the
model to synthesize heterogeneous expert judgments across diverse decision dimensions. The
decision process is optimized using a generalized score function, facilitating precise ranking and
assignment under uncertain and conflicting data.A real-world case study illustrates the model’s
applicability and robustness, demonstrating superior performance compared to conventional fuzzy
and interval-valued neutrosophic assignment frameworks. The proposed GNAM framework thus
advances neutrosophic decision science by providing a unified, extensible, and computationally
efficient tool for multi-criteria decision-making under deep uncertainty.