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Hospital reconversion in response to the COVID-19 pandemic using simulation and multi-objective genetic algorithms
Jaime Yair Perez-Tezoco1, Alberto Alfonso Aguilar-Lasserre1, Constantino Gerardo Moras-Sánchez1
1Division of Research and Postgraduate Studies, Tecnológico Nacional de México/Instituto Tecnológico de Orizaba, Av. Oriente 9, 852. Col. Emiliano Zapata, Orizaba 94320, México.
This study presents a simulation and optimization method for hospital reconversion during pandemics like COVID-19. The approach effectively reconfigures hospital layouts to improve patient flow and minimize infection risks.
Area of Science:
- Healthcare Management
- Operations Research
- Infectious Disease Preparedness
Background:
- The COVID-19 pandemic highlighted challenges in hospital capacity and the need for effective hospital reconversion strategies.
- Hospital reconversion is crucial for minimizing contagion risks among staff and patients and managing infectious healthcare waste.
- Existing methods may not fully integrate operational efficiency with safety protocols during pandemic-related restructuring.
Purpose of the Study:
- To develop and validate a methodology for hospital reconversion using simulation and mathematical optimization.
- To optimize hospital layouts by maximizing departmental proximity and minimizing agent flow costs.
- To incorporate medical personnel expertise into decision-making for pandemic-era hospital restructuring.
Main Methods:
- Development of a discrete event simulation model to analyze patient flow within hospital systems.
- Formulation of a mathematical optimization model using genetic algorithms to address hospital reconversion.
- Evaluation of optimization results through the simulation model and validation in a COVID-19 hospital setting.
Main Results:
- The proposed framework effectively reconfigures hospital departments, considering factors like elevator usage, location, and structural dimensions.
- The mathematical model demonstrated effectiveness in optimizing hospital layouts to enhance operational efficiency and safety during a pandemic.
- Incorporating medical expertise into the optimization process significantly improved decision-making for hospital reconversion.
Conclusions:
- The simulation and optimization methodology provides a robust framework for effective hospital reconversion during pandemics.
- This approach can be replicated across diverse hospital settings facing similar challenges.
- The study underscores the importance of integrating operational research with clinical expertise for resilient healthcare systems.
