This paper introduces a novel framework for optimizing delivery systems incorporating remotely piloted aircraft (RPAS), integrating advanced task allocation and routing loops to address key operational challenges in urban logistics. Inspired by studies on RPAS integration with public transportation networks for dynamic resource optimization, this work builds on a dual-loop structure: one dedicated to the spatiotemporal allocation of delivery tasks and the other to identifying optimal routes under complex urban conditions. Using public transit systems extends RPAS range, reduces energy consumption, and minimizes operational costs by utilizing existing infrastructure. The framework incorporates two independent optimization approaches, Monte Carlo simulations are employed to account for transit time variability, providing robust data for a multi-objective genetic algorithm that optimizes cost, time, and reliability, the cost model considers diverse components, including operational expenses, infrastructural investments for RPAS charging stations, and the potential economic impact on public transportation revenue. By comparing the results of the two approaches, the study evaluates their relative effectiveness in achieving significant delivery cost reductions and improving logistical performance. These findings underscore the potential of the dual-loop design to support practical decision-making in large-scale delivery network design.
