In disaster response logistics, it is critical to evaluate strategies for operational speed and efficiency and fairness in aid distribution. Therefore, we developed a simulation-based framework for assessing emergency delivery performance using the efficiency, effectiveness, and equity (3E) model under uncertainty. Using the Monte Carlo simulation v4.4.9 and MATLAB v4.4.9, the model tests a greedy resource allocation strategy across 100 randomized scenarios involving variable regional demand and travel times. Each scenario is evaluated based on total fulfillment, distribution balance, and delivery effort. The results indicate that under ideal conditions with sufficient supply and no logistical constraints, the strategy achieves full effectiveness and perfect equity, with consistent efficiency outcomes. While the system performs optimally in the base case, the model also highlights the importance of testing strategies under more constrained or disrupted environments. The proposed approach enables planners to assess performance trade-offs, providing a robust foundation for future extensions involving optimization, real-time data integration, or prioritization schemes.