A planning model for task assignment and energy optimization in UAV -Based mobile base stations for post-disaster communication

Ensuring the sustainability of communications infrastructure following natural disasters is of vital importance. Unmanned Aerial Vehicles (UAVs), equipped with base station technologies, provide that enables connectivity in disaster zones. While UAVs offer key advantages such as mobility, coverage, and prolonged airtime, their effective use in real-world scenarios is often constrained by operational factors such as battery capacity, adverse weather conditions, and dynamic disaster environments. This study introduces a Mixed-Integer Linear Programming (MILP) model designed to optimize the positioning and energy replenishment of UAV-based bases. The model incorporates dynamic user demand based on population density and disaster impact severity, battery limitations, and charging logistics. A hexagonal grid-based spatial framework is employed to maximize coverage in complex urban landscapes. The model is implemented using GAMS and its performance is evaluated through a case study, using district-level demographic and risk data. Scenarios are used to test the proposed solution method by varying the number of UAVs, deployment sites, and available charging stations. The results demonstrate the model's capability to produce effective and energy-efficient UAV assignment plans that significantly improve post-disaster communication coverage. Furthermore, the findings reveal that although the model performs well under moderate-scale conditions, execution times increase considerably in large-scale scenarios. This limitation suggests the necessity of integrating heuristic and metaheuristic approaches in future work to enhance real-time responsiveness and scalability. Overall, this study contributes a novel optimization framework that bridges theoretical modeling and practical deployment strategies, reinforcing the critical role of UAVs as not only a technological solution but also a humanitarian asset in disaster response systems.