Optimization of Low-Altitude Logistics Hub Site Selection and Capacity Allocation Based on Mixed Integer Linear Programming (MILP)

Aerodynamic noise and energy consumption are critical challenges in the operation of ultra-high-speed elevator systems. This study develops a computational-fluid-dynamics-based framework for aerodynamic analysis and multi-objective optimization. A three-dimensional flow-field model incorporating fluid-structure interaction and hybrid turbulence simulation is established to investigate aerodynamic behavior within elevator shafts. Key structural parameters are optimized through a surrogate-assisted genetic algorithm. Experimental and numerical results demonstrate substantial reductions in sound-pressure level, drag coefficient, and energy consumption. The framework provides an effective solution for aerodynamic optimization and energy-efficient transportation systems.