College of Air traffic Management, Civil Aviation University of China, Tianjin 300300, China

Abstract:

Existing research on vehicle-drone collaborative delivery often focuses on single-objective optimization, employs simplistic coordination mechanisms, and seldom considers multi-depot scenarios. To address these limitations, this paper develops a multi-objective optimization model for multi-depot vehicle-drone collaborative delivery. The model aims to minimize total cost, distance, and time, subject to constraints such as load capacity, drone range, time windows, and synchronization requirements.

A solution approach based on the NSGA-II algorithm is designed, incorporating composite encoding, multi-strategy population initialization, and improved genetic operations to enhance solution feasibility and diversity. Experimental results in a scenario with 4 depots and 36 customers show that the model generates 149 Pareto-optimal solutions, with cost ranging from 4.69 to 29.04 yuan (518.9% variation), distance from 148.48 to 202.56 km (36.4% variation), and time from 136.07 to 503.07 minutes (269.6% variation), demonstrating effective trade-offs among objectives.

The method efficiently produces feasible solutions across scales of 36 to 396 customers. Computation time increases from 189.99 seconds for 36 customers to 2560.68 seconds for 396 customers, with solution feasibility improving as scale expands. The results confirm the feasibility, effectiveness, and scalability of the proposed model and algorithm, supporting decision-making in logistics optimization.

Key words: ">low-altitude economy, vehicle-drone collaborative delivery, multi-depot, route optimization, NSGA-II algorithm