Version 1
: Received: 8 March 2024 / Approved: 11 March 2024 / Online: 11 March 2024 (21:14:51 CET)
How to cite:
Medina-Pérez, M.; Guzmán, G.; Saldana-Pérez, M.; Legaria-Santiago, V.K. Medical Support Vehicle Location and Deployment at Mass Casualty Incidents, Case Study: Mexico City. Preprints2024, 2024030600. https://doi.org/10.20944/preprints202403.0600.v1
Medina-Pérez, M.; Guzmán, G.; Saldana-Pérez, M.; Legaria-Santiago, V.K. Medical Support Vehicle Location and Deployment at Mass Casualty Incidents, Case Study: Mexico City. Preprints 2024, 2024030600. https://doi.org/10.20944/preprints202403.0600.v1
Medina-Pérez, M.; Guzmán, G.; Saldana-Pérez, M.; Legaria-Santiago, V.K. Medical Support Vehicle Location and Deployment at Mass Casualty Incidents, Case Study: Mexico City. Preprints2024, 2024030600. https://doi.org/10.20944/preprints202403.0600.v1
APA Style
Medina-Pérez, M., Guzmán, G., Saldana-Pérez, M., & Legaria-Santiago, V.K. (2024). Medical Support Vehicle Location and Deployment at Mass Casualty Incidents, Case Study: Mexico City. Preprints. https://doi.org/10.20944/preprints202403.0600.v1
Chicago/Turabian Style
Medina-Pérez, M., Magdalena Saldana-Pérez and Valeria Karina Legaria-Santiago. 2024 "Medical Support Vehicle Location and Deployment at Mass Casualty Incidents, Case Study: Mexico City" Preprints. https://doi.org/10.20944/preprints202403.0600.v1
Abstract
Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival in these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors such as insufficient prevention education, limited disaster assets, and inadequate coordination between public and private emergency services may complicate these situations. In large-scale emergencies, multiple hotspots are usually observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as an optimization problem, focusing on the location and allocation of resources in mass casualty incidents. The proposed case study is Mexico City in a post-earthquake scenario, using voluntary geographic information, open government data, and historical data from the September 19, 2017 earthquake. Additionally, it is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions.
Keywords
multiobjective optimization; mass casualty incidents; geographic information systems
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
