Delivery network design of a locker-drone delivery system

Bipan Zou, Siqing Wu, Yeming Gong (), Zhe Yuan and Yuqian Shi
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Bipan Zou: Zhongnan University of Economics and Law [China]
Siqing Wu: Zhongnan University of Economics and Law [China]
Yeming Gong: EM - EMLyon Business School
Zhe Yuan: PULV - Pôle Universitaire Léonard de Vinci
Yuqian Shi: Zhongnan University of Economics and Law [China]

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Abstract: Drones are increasingly used for last-mile delivery due to their speed and cost-effectiveness. This study focuses on a novel locker-drone delivery system, where trucks transport parcels from the warehouse to lockers, and drones complete the final delivery. This system is ideal for community and intra-facility logistics. The research optimises the network design by determining the location of lockers, the number of drones at each locker, and the assignment of demands to lockers, minimising operating costs. Both single-parcel and multi-parcel capacity drones are examined. We build an optimisation model for each system, considering drone service capacity as a critical constraint. We design an algorithm combining average sample approximation and a genetic algorithm to address demand uncertainty. The algorithm's efficiency is validated through comparative analysis with Gurobi. Numerical experiments, using real and generated data, optimise the network design. Results show that the multi-capacity drone system requires fewer lockers and drones than the single-capacity system. Although the single-capacity system yields lower drone delivery costs, it incurs higher truck delivery costs. Additionally, a comprehensive cost analysis compares the cost-efficiency of the locker-drone system with a conventional drone delivery system, revealing the cost-saving advantage of the locker-drone system.

Keywords: Drone; logistics; sample average approximation; genetic algorithm; last-mile delivery (search for similar items in EconPapers)
Date: 2024-06-02
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Published in International Journal of Production Research, 2024, 62 (11), 4097-4121 p. ⟨10.1080/00207543.2023.2254402⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04343875

DOI: 10.1080/00207543.2023.2254402

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