Details

Autor(en) / Beteiligte

• Almeida Coco, Amadeu
• Duhamel, Christophe
• Santos, Andréa Cynthia
• Haddad, Matheus Nohra

Titel

Solving the probabilistic drone routing problem - searching for victims in the aftermath of disasters

Ist Teil von

• Networks, 2024

Ort / Verlag

Wiley

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Several major industrial disasters happen each year around the world. They usuallyinvolve limited accessibility, poor ground conditions and toxic wastes. As a consequence, thisreduces the efficiency of humanitarian operations. In such a context, flying drones may be aviable alternative: faster, no dependency on ground conditions, and larger areas scanned. Theyare also better suited for following the population and the crisis dynamic. For such a purpose,various issues have to be addressed such as defining and optimizing the drone’s routes, theirenergy consumption, choosing the relay points for recharging equipment, among others. Inthis study, several additional features from existing works are considered: first, a probabilityof identifying individuals is defined. Thus, each node can be scanned several times in order toimprove the observation. In addition, the nodes are prioritized according to a given heatmap.The Probabilistic Drone Routing Problem (PDRP) consists of finding a route, i.e. a sequenceof trips, for each drone such that the sum of the expected number of identified individuals onall routes is maximized. Constraints on energy consumption, collision avoidance and drone-base assignment are considered. We propose a heuristic and metaheuristics based on theAdaptive Large Neighborhood Search for the PDRP. The methods are tested on theoreticalinstances, as well as on a case study of the Beirut Port explosion on August 4, 2020, in orderto analyze the performance of the proposed methods.