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

Background: In the context of humanitarian logistics, efficiently evacuating people from disaster-stricken areas is a complex challenge. This study focuses on the Tula River region in Hidalgo, Mexico, exploring the evacuation and support of individuals in temporary shelters. Despite the fact that th...

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Bibliographic Details
Published in:Logistics 2024-01, Vol.8 (1), p.8
Main Authors: Santana-Robles, Francisca, Hernández-Gress, Eva Selene, Martínez-López, Ricardo, González-Hernández, Isidro Jesús
Format: Article
Language:English
Subjects:
aid collection strategy
Costs
Decision making
Disaster relief
Disasters
Drainage
Evacuation of civilians
evacuation of people
Evacuations & rescues
Floods
Forest & brush fires
Genetic algorithms
Humanitarian aid
Humanitarianism
Inventory
Inventory management
Linear programming
Literature reviews
Logistics
Methods
Mexico
Optimization
Planning
Rio Tula Flood
shelter location
Stormwater management
Supply chains
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Summary:Background: In the context of humanitarian logistics, efficiently evacuating people from disaster-stricken areas is a complex challenge. This study focuses on the Tula River region in Hidalgo, Mexico, exploring the evacuation and support of individuals in temporary shelters. Despite the fact that the topic has been addressed in the literature, it is necessary to have quick response methods that can be used by decision-makers to adapt and utilize existing spaces as temporary shelters, in addition to knowing how to evacuate people. Methods: Addressing this void, a methodology to minimize evacuation and aid distribution costs is introduced. Leveraging existing algorithms, particularly Integer Linear Programming, the model determines shelter activation and utilizes the Vehicle Routing Problem to assess aid delivery strategies. Results: The research identifies optimal evacuation routes from 13 affected areas to 34 shelters and analyzes aid distribution costs under various demand scenarios: original, increased, and decreased by 10%, based on the number of transport units allocated and Google Maps distances. It also evaluates the costs associated with humanitarian aid distribution under varying collection strategies, involving state and municipal governments. Conclusion: This approach provides a decision-making foundation and can be adapted for similar analyses in other communities during extreme events.
ISSN:2305-6290
2305-6290
DOI:10.3390/logistics8010008