Collaborative delivery problem of trucks and multiple drones: an en‐route operation with flexible launch positions

Abstract Drones are gradually being used in logistics, and collaborative systems combining trucks and drones are proposed for last‐mile delivery. Therefore, it is a meaningful study to improve the efficiency of collaborative delivery of truck–drone system. Based on the truck–drone collaborative delivery problem with en‐route operations, this paper introduces a new concept of flexible launch point, which is to set up multiple non‐customer stops in the truck's route for launching drones. In light of this concept, an en‐route operation with flexible positions in the truck–drone collaborative delivery problem is proposed. The problem is formulated as a mixed integer linear program that aims to minimize customer waiting times, determine the optimal route for the truck with multi‐drones, and establish the optimal flexible launch positions for the drones. We developed a variable neighborhood search algorithm and conducted large‐scale experiments. The experiments have demonstrated that the new collaborative approach maximizes drone utilization, expands the range of drone services, and ensures flexible launch. The en‐route operation with flexible launch positions presented in this paper provides innovative ideas for further application of trucks with multi‐drones in last‐mile delivery. This is also relevant for other scenarios, such as emergency management and routine inspections, which involve collaboration between trucks and drones.

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Publication Details

Journal
International Transactions in Operational Research
Published
2025-05-05
DOI
https://doi.org/10.1111/itor.70045
Primary Topic
UAV Applications and Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

Collaborative delivery problem of trucks and multiple drones: an en‐route operation with flexible launch positions

Tianze Chi, Bing Han, Zhengyi Sun
International Transactions in Operational Research
UAV Applications and Optimization
article

Collaborative delivery problem of trucks and multiple drones: an en‐route operation with flexible launch positions

Tianze Chi, Bing Han, Zhengyi Sun
article en

Abstract

Abstract Drones are gradually being used in logistics, and collaborative systems combining trucks and drones are proposed for last‐mile delivery. Therefore, it is a meaningful study to improve the efficiency of collaborative delivery of truck–drone system. Based on the truck–drone collaborative delivery problem with en‐route operations, this paper introduces a new concept of flexible launch point, which is to set up multiple non‐customer stops in the truck's route for launching drones. In light of this concept, an en‐route operation with flexible positions in the truck–drone collaborative delivery problem is proposed. The problem is formulated as a mixed integer linear program that aims to minimize customer waiting times, determine the optimal route for the truck with multi‐drones, and establish the optimal flexible launch positions for the drones. We developed a variable neighborhood search algorithm and conducted large‐scale experiments. The experiments have demonstrated that the new collaborative approach maximizes drone utilization, expands the range of drone services, and ensures flexible launch. The en‐route operation with flexible launch positions presented in this paper provides innovative ideas for further application of trucks with multi‐drones in last‐mile delivery. This is also relevant for other scenarios, such as emergency management and routine inspections, which involve collaboration between trucks and drones.

International Transactions in Operational Research
Waseda University (JP), Dalian Maritime University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 87%
UAV Applications and Optimization
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Collaborative delivery problem of trucks and multiple drones: an en‐route operation with flexible launch positions — Tianze Chi, Bing Han, et al. · International Transactions in Operational Research (2025) | TGRS Research Map | TGRS