1036. Truck-Drone Arc Covering Problem: Application and Case Study in Disaster Management
Invited abstract in session MD-17: Drone Routing, stream Combinatorial Optimization.
Monday, 14:30-16:00Room: Esther Simpson 2.08
Authors (first author is the speaker)
| 1. | Alexander Rave
|
| Ingolstadt School of Management, Catholic University of Eichstätt-Ingolstadt | |
| 2. | Pirmin Fontaine
|
| Ingolstadt School of Management, Catholic University of Eichstätt-Ingolstadt |
Abstract
Effective river exploration before, during, or after floods is crucial for civil protection and disaster management, helping to enhance preparedness and even prevent disasters. Traditionally, this process relies on boats, trucks, helicopters, or is sometimes not conducted at all. However, autonomous drones equipped with cameras can significantly improve river monitoring. By integrating a truck with a drone, the drone’s operational flexibility increases, overcoming its limited range. Recognizing this potential, the Bavarian Red Cross has equipped a truck with a drone for river coverage.
Inspired by this real-world application, we introduce the Truck-Drone Arc Covering Problem (TD-ACP) to optimize river exploration. We model the TD-ACP as a mixed-integer linear program (MILP) and incorporate valid inequalities that strengthen the formulation, enabling the solution of realistically sized instances to optimality. Our numerical study shows that using drones for river coverage can reduce coverage time by at least 56.3% compared to traditional boat coverage. Additionally, we propose a manual planning heuristic that is easily applicable for practitioners, achieving an average optimality gap of just 4.2%.
Keywords
- Programming, Mixed-Integer
- Vehicle Routing
- Disaster and Crisis Management
Status: accepted
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