A Priority-Optimized Aggregation framework for Multi-Expert Multi-Criteria  Neutrosophic Assignment Problems

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:

Neutrosophic Assignment Problem, Multi-Expert, Multi-Criteria, Priority Optimized Mean, Uncertainty, Aggregation, Decision-Making.

Abstract

In many real-world situations, decision-makers must allocate limited resources while dealing
with uncertainty, differing expert opinions, and multiple evaluation criteria. Traditional
assignment models, including fuzzy approaches, often struggle to represent indeterminacy in a
clear and structured manner. To overcome this limitation, this study presents a Priority
Optimized Aggregation Framework for solving Multi-Expert Multi-Criteria Neutrosophic
Assignment Problems (MEMCNAP).
The proposed approach combines expert credibility and criterion importance through a two
stage Priority-Optimized Mean (POM) operator. A weighted neutrosophic score function is
then used to transform the aggregated neutrosophic evaluations into scalar values that can be
optimized using classical assignment techniques. The model is formulated in a generalized
manner so that it can accommodate any number of experts, criteria, agents, and tasks.
To illustrate its practical relevance, a construction contractor allocation problem is examined
in detail. The results demonstrate that the framework effectively manages uncertainty,
minimizes information distortion during aggregation, and supports transparent and logically
consistent assignment decisions. By incorporating structured priority handling at both the
expert and criteria levels, the proposed model offers a meaningful extension to existing
neutrosophic assignment approaches.