A Model and Case Study for Optimizing the Schedule of Wildfire Surveillance Drones

Drone-based wildfire surveillance has gained significant attention due to its ability for rapid deployment and real-time data collection in remote and hard-to-access areas. However, current surveillance activities are limited to specific locations and times, making it challenging to comprehensively monitor all wildfire-prone regions. This study addresses the optimization problem of enhancing the operational efficiency of drone-based wildfire surveillance. Specifically, it focuses on optimizing the hourly operational schedules of drones during the planning period, considering the fluctuating surveillance demands over time. Drones are deployed from drone stations to perform surveillance for a certain duration and must return to the station for battery recharging before resuming operations. Therefore, it is crucial to effectively plan the flight and charging schedules for multiple drones to maximize the satisfaction of surveillance demands. To this end, a mixed-integer programming (MIP) model was developed. A case study was conducted focusing on the Gangwon-do region, including Inje, Chuncheon, and Hongcheon. The results demonstrate that the proposed model can be effectively applied to real-world wildfire surveillance scenarios, significantly improving both surveillance efficiency and resource utilization. This study provides valuable insights into advancing drone-based wildfire surveillance systems and offers practical strategies for efficient resource management.