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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.

Computers & Industrial Engineering

|April 15, 2024

Summary

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.

Keywords:

COVID-19 Discrete event simulation Hospital reconversion Multi-objective genetic algorithm

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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.