Multi‐Objective Route Optimization for Drone Deliveries Integrated With Public Transportation

To enhance delivery efficiency while supporting urban public transportation development, this study investigates a bus‐assisted drone delivery route optimization problem within the context of the low‐altitude economy. A multi‐objective optimization model is formulated to minimize both distribution cost and time. To solve this problem efficiently, we propose a non‐dominated sorting variable neighborhood search (NSVNS) algorithm. To validate the algorithm's performance, we conduct a dispersion analysis to assess its stability and demonstrate its superiority over NSGA‐II in solution diversity, distribution uniformity, and overall effectiveness. Additionally, experimental analysis confirms the effectiveness of the proposed neighborhood search operators. Finally, a case study is conducted based on Chongqing city's line 100 bus route and surrounding customer locations to derive optimal drone delivery routes. Sensitivity analysis indicates that increasing drone payload capacity has a more significant impact on delivery efficiency than extending drone range, providing insights for optimizing drone‐based urban logistics.