Computers & Industrial Engineering · 5 authors, 3 centres
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A discrete event simulation model was combined with a multi-objective NSGA-II genetic algorithm to optimize the layout and department assignment during hospital reconversion for COVID-19 at a regional hospital in Mexico. The Pareto-optimal solutions were evaluated via simulation and the best selected using TOPSIS, demonstrating the framework's effectiveness in addressing hospital reconversion under pandemic conditions.
This methodology study proposes a simulation-optimization-simulation framework for hospital reconversion during health contingencies such as COVID-19. A discrete event simulation model was first developed to study patient flow within the hospital. A multi-objective mathematical model was then formulated to simultaneously maximize adjacency requirements between departments and minimize flow costs of agents (doctors, nursing, support staff). The model was optimized using NSGA-II, generating a Pareto front of non-dominated solutions, which were evaluated through simulation and the final solution selected via TOPSIS. The framework was applied to IMSS HGR Orizaba, a regional general hospital in Mexico. Results demonstrated the model's effectiveness in incorporating medical personnel's expertise regarding elevator use, department location, structural dimensions, corridor use, and floor assignment. The approach can potentially be replicated in hospitals with different characteristics.
