Consistency Preserving MOORA Framework for Robust Educational Admission and Healthcare Triage

Abstract

Effectively distributing scarce resources presents a major challenge for governance in both competitive school admissions and emergency medical triage. The main problem lies in the instability of conventional ranking algorithms, where even small changes in data or the addition of new candidates often lead to rank reversals. This instability undermines the fairness of student admissions and the safety of patient prioritization. To tackle this problem, this study introduces a consistency-preserving Intelligent Decision Support System based on Multi-Objective Optimization by Ratio Analysis (MOORA). Unlike approaches that depend on linear normalization, this framework employs Euclidean vector normalization to successfully separate subjective weights from objective performance values. The proposed model is tested using a high-dimensional dataset of 340 educational applicants and a simulated healthcare triage scenario of similar size. Experimental results show that the framework maintains a ranking consistency correlation above 0.90 with established baselines while achieving a 0.00% rank reversal rate in scenarios with conflicting criteria. These findings confirm that the proposed algorithmic structure provides a mathematically sound and domain-independent logic for critical institutional decision-making.