Collaborative Hybrid Aerial and Ground Vehicle Routing for Post-Disaster Assessment
Hybrid aerial and ground vehicles are seen as a promising option for deployment in a post-disaster assessment due to the risk of infrastructure damage that may hinder the assessment operation. The efficient operation of the hybrid aerial and ground vehicle, particularly routings, remains a challenge...
Guardado en:
Autores principales: | , , , |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/bc5f1890fbbb48dfb50506a0725f68e5 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Sumario: | Hybrid aerial and ground vehicles are seen as a promising option for deployment in a post-disaster assessment due to the risk of infrastructure damage that may hinder the assessment operation. The efficient operation of the hybrid aerial and ground vehicle, particularly routings, remains a challenge. The present study proposed a collaborative hybrid aerial and ground vehicle to support the operation of post-disaster assessment. The study developed two models, i.e., the Two-Echelon Vehicle Routing Problem combined with Assignment (2EVRPA) and the Two-Echelon Collaborative Vehicle Routing Problem (2ECoVRP) to evaluate optimal routings for both aerial and ground vehicles. The difference lies in the second echelon in which the 2EVRPA uses a single point-to-point assignment, whereas the 2ECoVRP considers the collaborative routings between the ground vehicle and the aerial vehicle. To demonstrate its applicability, the developed models were applied to solve the post-disaster assessment for the Mount Merapi eruption in Yogyakarta, Indonesia. Sets of numerical experiments based on the empirical case were conducted. The findings indicate that the 2ECoVRP performs better than 2EVRPA in terms of the total operation time. The tabu search algorithm was found to be a promising method to solve the models due to its good quality solution and computational efficiency. The deployment of eight drones appears to be optimum for the given network configuration of the studied case. Flight altitude and battery capacity were found to be influential to the operation time, hence requiring further exploration. Other potential avenues for future research are also discussed. |
---|