Transforming last mile delivery with heterogeneous assistants: drones and delivery robots

Cheng, Chen, Demir, Emrah ORCID: https://orcid.org/0000-0002-4726-2556, Hu, Xisheng and Huang, Hainan 2025. Transforming last mile delivery with heterogeneous assistants: drones and delivery robots. Journal of Heuristics 31 , 8. 10.1007/s10732-024-09543-0 Item availability restricted.

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Abstract

With the rapid global expansion of e-commerce and the increasing number of online shoppers, logistics service providers (LSPs) are exploring sustainable solutions to meet the rising demand. Thanks to developments in automation and robotic technologies, LSPs have now the opportunity to enhance their operations through the deployment of autonomous delivery solutions like drones and delivery robots. This paper investigates a practical delivery system to integrate these emerging technologies simultaneously into conventional van-only delivery system. Additionally, the effects of various assistant characteristics on operations are examined through broader assumptions. We introduce a mathematical model aiming to minimize delivery makespan and explore various valid inequalities to mitigate its complexity. A new hybrid metaheuristic algorithm combining genetic algorithm and large neighborhood search algorithm is also proposed for large scale instances. A threelayer coding and encoding method is also introduced for genetic algorithm to manage the complex structure of the problem. Finally, extensive numerical experiments are conducted to show the effectiveness of valid inequalities and the algorithm. The sensitivity analyses provide comparisons of various delivery configurations and offer valuable insights for the logistics industry to integrate these innovative delivery solutions into their daily operations. In our experiments, using a single drone reduces total delivery times by up to 23.57%, while a single robot contributes to a 37.19% improvement in the objective. The heterogeneous configuration offers a substantial 49.71% improvement compared to using only vans for deliveries.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Business (Including Economics)
Publisher:	Springer
ISSN:	1381-1231
Date of First Compliant Deposit:	2 December 2024
Date of Acceptance:	2 December 2024
Last Modified:	03 Mar 2025 15:00
URI:	https://orca.cardiff.ac.uk/id/eprint/174433

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