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

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 twostage 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.