Aggregation of Expert Ranking Opinions Using Multi-Criteria Decision-Making Methods

This paper investigates the application of multi-criteria decision-making methods (MCDM) for the aggregation of expert opinions related to ranking, considering the weight coefficients of the experts' competencies. Measurement Alternatives and Ranking according to Compromise Solution (MARCOS), Complex Proportional Assessment (COPRAS), Additive Ratio Assessment (ARAS), and Combined Compromise Solution (CoCoSo) methods, which are traditionally used to select the optimal alternative in MCDM, are applied in this study for the purpose of aggregating the ranks given by experts. The validity of the results was confirmed by comparison with the Einstein weighted arithmetic average (EWAA), Hamacher weighted arithmetic aggregation (HWAA), Ordered Weighted Averaging (OWA), and Ordered Weighted Geometric (OWG) operators, where identical ranks were obtained. The conducted sensitivity analysis based on a Monte Carlo simulation confirms that classical MCDM methods provide stable and reliable results under conditions of perturbations of expert weights and ranks, while aggregation operators show significantly lower robustness and consistency. The aforementioned shows that MCDM methods can be a reliable and practical tool for group decision-making, i.e., the aggregation of expert opinions, with the advantage of generating final ranks without additional post-analysis.