This study investigates enhancements to the Ranking Comparison (RANCOM) method for group decision-making in Multi-Criteria Decision Analysis (MCDA) contexts. Two key modifications are explored: assigning weights to decision-makers to reflect differences in expertise and influence, and examining a ratio-based mechanism for more nuanced modeling of pairwise criteria comparisons. Through extensive simulation experiments involving varying numbers of alternatives, criteria, and decision-makers, and incorporating different levels of opinion divergence, the performance of these enhancements is evaluated using the Weights Similarity Coefficient (WSC). Results show that the weighted approach yields a more accurate aggregation of preferences, particularly in problems with a greater number of criteria and when opinions among decision-makers diverge. Furthermore, the ratio-based mechanism outperforms the traditional three-value dictionary in most cases, especially as problem complexity increases. These findings suggest that incorporating both decision-maker influence and more nuanced criteria comparisons can significantly improve the robustness and precision of group decision-making methods.