Journal of Zhejiang University SCIENCE  A

Accepted manuscript available online (unedited version)


Bi-level collaborative optimization for unmanned aerial vehicle logistics hub location and delivery routing


Author(s):  Zhenhe YU1, Xiuguo ZOU1, Li LI2

Affiliation(s):  1College of Smart Agriculture (College of Artificial Intelligence), Nanjing Agricultural University, Nanjing 210095, China 2Department of Automation, Tsinghua University, Beijing 100084, China

Corresponding email(s):  Li LI, li-li@mail.tsinghua.edu.cn

Key Words:  unmanned aerial vehicle (UAV); logistics hub location selection optimization; route optimization; bilevel collaborative optimization


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Zhenhe YU1, Xiuguo ZOU1, Li LI2. Bi-level collaborative optimization for unmanned aerial vehicle logistics hub location and delivery routing[J]. Journal of Zhejiang University Science A,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.A2500615

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Abstract: In view of problems associated with existing unmanned aerial vehicle (UAV) logistics systems, such as poor coupling between logistics hub locations and delivery routes and insufficient responsiveness to dynamic service demands, in this paper, a bilevel coupled model-based method for the collaborative optimization of UAV logistics hub location selection and route planning is proposed. In the lower level of the model, the adaptive large neighborhood search (ALNS) algorithm combined with the A* algorithm is employed to optimize the delivery path for a given logistics hub location. In the upper level of the model, a genetic algorithm (GA) is employed to optimize the hub location on the basis of the optimal path that is provided by the lower layer, together with the surrounding environmental conditions and the logistics hub construction costs. The scheme undergoes continuous iterative optimization via the dynamic coupling of the upper and lower layers. Experimental results demonstrate that the proposed method yields rational hub locations and effectively integrates the optimization of hub siting and transportation routing, achieving superior performance compared to baselines.

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On-line Access: 2026-05-07

Received: 2025-11-24

Revision Accepted: 2026-04-08

Crosschecked: 0000-00-00

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714

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