Quick-Response Model for Pre- and Post-Disaster Evacuation and Aid Distribution: The Case of the Tula River Flood Event

In humanitarian logistics, efficiently evacuating people during disasters poses a significant challenge. Comprehensive plans are essential for moving individuals from affected areas, encompassing both pre- and post-disaster phases. These plans must optimize resource usage, including public transportation, and consider those with mobility challenges. This paper focuses on evacuating and assisting individuals in temporary shelters, specifically in the Tula River region, Hidalgo State, Mexico. Despite the recurring nature of this challenge, there is a noticeable gap in the scientific literature addressing quick-response methods for decision-makers to adapt existing spaces as temporary shelters and efficiently evacuate people from risk areas. To bridge this gap, we introduce a methodology aiming to minimize evacuation and aid distribution costs. Leveraging established algorithms like integer linear programming, the model determines shelter activation, while the vehicle routing problem assesses aid delivery strategies. Our research identified optimal evacuation routes from 13 affected areas to 34 shelters and aid distribution from collection centers to affected zones. We analyzed the average evacuation times for different demand scenarios: original, increased by 10%, and decreased by 10%, considering the transport units allocated and the distances on Google Maps. This study also evaluates the costs associated with humanitarian aid distribution under varying collection strategies involving state and municipal governments.