A Hybrid Genetic Algorithm on Routing and Scheduling for Vehicle-Assisted Multi-Drone Parcel Delivery
| Autor: | Yan Dong, Fang Lu, Kai Peng, Menglan Hu, Jingxuan Du, Pan Zhou, Qianguo Sun |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
education.field_of_study
General Computer Science Computer science Distributed computing Population General Engineering Initialization ComputerApplications_COMPUTERSINOTHERSYSTEMS 020206 networking & telecommunications Unmanned aerial vehicle 02 engineering and technology Parcel delivery Drone Scheduling (computing) routing cargo delivery 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing General Materials Science scheduling lcsh:Electrical engineering. Electronics. Nuclear engineering education lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 7, Pp 49191-49200 (2019) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2019.2910134 |
| Popis: | In recent years, the unmanned aerial vehicles (UAVs) have exhibited significant market potential to greatly reduce the cost and time in the field of logistics. The use of UAVs to provide commercial courier has become an emerging industry, remarkably shifting the energy use of the freight sector. However, due to limited battery capacities, the flight duration of civilian rotorcraft UAVs is still short, hindering them from performing remote jobs. In this case, people customarily utilize ground vehicles to carry and assist UAVs in various applications, including cargo delivery. Most previous studies on vehicle-drone cooperative parcel delivery considered only one UAV, thereby suffering from low efficiency when serving a large number of customers. In this paper, we propose a novel hybrid genetic algorithm, which supports the cooperation of a ground vehicle and multiple UAVs for efficient parcel delivery. Our routing and scheduling algorithm allows multiple UAVs carried by the vehicle to simultaneously deliver multiple parcels to customers residing in different locations. The proposed algorithm consists of a pipeline of several modules: population management, heuristic population initialization, and population education. The performance evaluation results show that the proposed algorithm has significant efficiency over existing algorithms. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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