Urban air mobility increasingly relies on autonomous multicopter fleets whose operational sustainability remains constrained by the absence of intelligent recharging infrastructures. This study introduces a simulation based decision intelligence model designed to evaluate six multicopter charging station archetypes under smart city conditions. The proposed framework integrates five normalized evaluation factors, namely Security, Infrastructure Cost, Logistics Compatibility, Smart City Integration, and Sustainability, within a transparent and auditable multi-criteria decision framework. Two complementary evaluation modes are developed to ensure analytical rigor and interpretability. The first mode, Mode A, represents a reproducible baseline configuration that employs equal weighting to retained methodological clarity. The second mode, Mode B, functions as a bounded coordination operator that establishes a controlled relationship between infrastructure capacity and logistics flow, enabling interaction informed evaluation without altering the ranking logic. Synthetic decision data are generated through Latin Hypercube Sampling, while bootstrap resampling is used to quantify uncertainty. The stability of both modes is analytically verified, showing that Kendall’s τ exceeds 0.90 and Top-k retention remains above 95 percent. These results demonstrate that introducing interaction awareness refines interpretability while maintaining analytical consistency across uncertainty ranges. The findings reveal that Last Mile and First Mile stations maintain the highest composite efficiency scores, 0.82 and 0.80 respectively, across various urban morphologies. Roof and Electric Vehicle Coupled configurations also display competitive scalability and improved performance when aligned with renewable energy scenarios. The overall framework provides a reproducible, policy aligned, and scientifically traceable foundation for the planning, deployment, and empirical calibration of urban drone charging networks. It further establishes a consistent methodological pathway for decision making in data scarce environments, ensuring that analytical transparency and operational relevance are sustained throughout future pilot implementations.
Çelik et al. (Fri,) studied this question.