Cooperative route planning for the drone and truck in delivery services: A bi-objective optimisation approach

Abstract

The deployment of drones to support the last-mile delivery has been initially attempted by several companies such as Amazon and Alibaba. The complementary capabilities of the drone and the truck pose an innovative delivery mode. The relevant optimisation problem associated with this new mode, known as the travelling salesman problem with drone (TSP-D), aims to find the coordinated routes of a drone and a truck to serve a list of customers. In practice, managers sometimes intend to attain a compromise between operational cost and completion time. Therefore, this article addresses a bi-objective TSP-D considering both objectives. An improved non-dominated sorting genetic algorithm (INSGA-II) is proposed to solve the problem. Specifically, the label algorithm-based decoding method, the fast non-dominated sorting approach, the crowding-distance computation procedure, and the local search component are devised to accommodate the features of the problem. Furthermore, the first Pareto front obtained by the INSGA-II is improved by a post-optimisation component. Computational results validate the competitive performance of the proposed algorithm. Meanwhile, the trade-off analysis demonstrates the relationship between operational cost and completion time and provides managerial insights for managers designing reasonable compromise routes.

Keywords:

• Logistics
• travelling salesman problem
• multi-objective optimisation
• drone-assisted delivery
• metaheuristics

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 https://en.wikipedia.org/wiki/Unmanned_aerial_vehicle

2 https://www.cbsnews.com/news/amazon-unveils-futuristic-plan-delivery-by-drone/

3 https://www.nbcnews.com/tech/innovation/special-delivery-watch-googles-project-wing-drones-airdrop-packages-n447946

4 http://money.cnn.com/2015/02/04/technology/alibaba-delivery-drones/index.html

5 https://baijiahao.baidu.com/s?id=1617635422073448413&wfr = spider&for = pc

Additional information

Funding

This work is supported by research grants from the National Natural Science Foundation of China (71602081, 71801058), Guangxi Natural Science Foundation (2018GXNSFBA281104) and the Belt and Road Special Project of Lanzhou University (2018ldbryb015).