An ellipse-based locating method for flexible deployment of emergency UAVs

• Published in: Socio-economic planning sciences 2024-12, Vol.96, p.102049, Article 102049
• Main Authors: Zhao, Jinqiu, Yu, Le, Xie, Binglei
• Format: Article
• Language: English
• Subjects: Benders decomposition algorithm; Ellipse coverage; Emergency depot location; Flexible deployment of UAVs; UAV reachability
• ISSN: 0038-0121
• DOI: 10.1016/j.seps.2024.102049

Unmanned Aerial Vehicles (UAVs), or drones, are gaining attention in emergency response for their rapid mobility in dynamic scenarios. Constrained by limited endurance and payload, UAVs typically operate in a ”depot-customer-depot” paradigm. Thus, optimally locating multiple depots is critical to achieving operational efficiency and flexibility. Traditional location models, which rely on circular coverage, fail to capture the actual reachable area for UAV round-trips between multiple depots within a given endurance range. This drawback restricts deployment flexibility or results in excessive redundancy, even making it impractical. To address this limitation, we introduce an ellipse-based locating method for flexible UAV deployment, inspired by UAV reachability and process flexibility in manufacturing. This approach attempts to optimize the redundancy of multi-depot coverage for demand points to achieve a better balance between deployment flexibility and resource requirements. To tackle the model’s computational challenge, we present an improved Benders decomposition algorithm that speeds up the solution process by analytically addressing subproblems and implementing dominance rules to manage the master problem’s size. Simulations show that the proposed model greatly improves the ability to handle uncertainties by incorporating slight redundancy in emergency resources, and the fulfillment rate of demand fluctuations is increased by 5%–20%, which shows the superiority of enhancing the mobility and flexibility of UAV deployment. •Introduced a discrete e-pCP locating model and proved its NP-hardness.•An improved Benders algorithm speeds up the solution process.•Numerical simulations and a case study are conducted.•Multi-depot trips enhance UAV reachability and deployment flexibility.•The e-pCP model better balances deployment flexibility and resource requirements.