IBSS Study: Smart UAV Routes for Disaster Monitoring

18 Aug 2025

Recently, a paper entitled “Energy-Efficient UAV routing problem based on approximate cellular decomposition for geohazards monitoring”, co-authored by Dr Jianyu Xu from the International Business School Suzhou (IBSS) at Xi’an Jiaotong-Liverpool University (XJTLU) along with their collaborators, has been accepted and published in Computers and Operations Research, a journal ranked ABS 3 and Tier 1*. This study focuses on key challenges in disaster prevention, aiming to provide innovative solutions for hazard prevention by efficiently utilizing unmanned aerial vehicles (UAVs) to monitor areas prone to landslides, floods, and other geological disasters. Given the limited battery life of UAVs and the inability of their onboard cameras to cover large areas in a single scan, this research has developed an intelligent routing system that ensures full coverage of hazardous zones while minimizing energy consumption.

The paper proposes a novel method for planning UAV routes, which centrally involves analyzing energy consumption differences across various flight modes (straight flight, turning, and hovering). Additionally, it decomposes large hazardous areas into smaller grid units to ensure UAVs can accurately collect all necessary data without wasting battery power. To solve this complex optimization problem, the researchers designed a hybrid algorithm (EGHM) capable of quickly calculating the most efficient flight paths. Tests based on real-world data from Shaanxi Province, China, have verified the significant effectiveness of this method—it achieves full coverage of all designated areas while effectively reducing energy consumption. The study also delves into the impact of different battery capacities on system performance, providing a scientific reference for managers to determine the optimal UAV fleet size that meets practical needs.

For supply chain and operations managers, this research holds practical value in multiple aspects. In terms of operational efficiency, the system achieves full-area coverage by reducing unnecessary movement, directly lowering the number of flights, battery consumption, and overall costs. The energy consumption model can accurately predict the UAVs' airborne endurance time, facilitating more rational task planning. Regarding cost and scalability, the study finds that using UAVs with larger batteries can reduce the total number of UAVs needed, but it is necessary to balance the potential increase in individual UAV energy consumption, offering quantitative basis for equipment investment decisions. In terms of technical deployment, this routing method is compatible with existing UAV sensors, requiring no additional expensive upgrades, and can flexibly adapt to environmental changes, making it highly applicable in actual disaster monitoring. From the perspective of risk management, the system can effectively prevent UAVs from running out of power mid-mission, and this high reliability is crucial for emergency response teams and institutions managing high-risk areas.

Dr Jianyu Xu is currently an Associate Professor of IBSS at Xian-Jiaotong Liverpool University. Dr Xu got his Bachelor degree in Statistics from Peking University in 2012. Later, he got his Phd degree jointly in Statistics from Chinese Academy of Sciences and in System Engineering and Engineering Safety from City University of Hong Kong. His research interest is in reinforcement learning, quality and reliability engineering and industrial statistics.

Computers & Operations Research (ABS 3, Tier 1*, IF=4.1) is a privileged forum for state-of-the-art work exploring the theory and practice of Operations Research (OR) intertwined with advanced computational methodologies. The journal publishes high-quality innovative and impactful research articles in theories, modeling, algorithms, and applications of Operations Research.

18 Aug 2025

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