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<p>The COVID-19 pandemic underscored the need for flexible and efficient emergency department (ED) layouts to manage patient surges and complex care demands. This paper presents a method for optimizing ED layouts that improves both operational efficiency and pandemic preparedness. We present a mathematical model for optimizing patient flow and processes, which is crucial for dynamic crisis response. We approach the Emergency Department Layout (EDL) problem with Particle Swarm Optimization (PSO) and a heuristic-based strategy to generate an effective initial layout. This combination improves convergence and facilitates the identification of optimal solutions. The method focuses on minimizing travel distances, improving department adjacency, and increasing surge capacity, particularly in pandemic scenarios. Comparative analysis with Genetic Algorithms (GA) confirms the superior performance of the proposed PSO approach in terms of speed, solution quality, and adaptability. The model is used to a case study based on the Roanne Hospital’s ED Layout, proving efficacy and scalability. This study underlines the importance of intelligent layout planning in improving emergency department performance and healthcare resilience during future pandemics.</p>

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Adaptive emergency department layouts for pandemic preparedness: a particle swarm optimization approach

  • 期刊论文

  • 发表日期：

    2025年7月18日

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  • Khalil Bouramtane,
  • Said Kharraja,
  • Jamal Riffi,
  • Omar El Beqqali,
  • Saïd Boujraf

摘要

The COVID-19 pandemic underscored the need for flexible and efficient emergency department (ED) layouts to manage patient surges and complex care demands. This paper presents a method for optimizing ED layouts that improves both operational efficiency and pandemic preparedness. We present a mathematical model for optimizing patient flow and processes, which is crucial for dynamic crisis response. We approach the Emergency Department Layout (EDL) problem with Particle Swarm Optimization (PSO) and a heuristic-based strategy to generate an effective initial layout. This combination improves convergence and facilitates the identification of optimal solutions. The method focuses on minimizing travel distances, improving department adjacency, and increasing surge capacity, particularly in pandemic scenarios. Comparative analysis with Genetic Algorithms (GA) confirms the superior performance of the proposed PSO approach in terms of speed, solution quality, and adaptability. The model is used to a case study based on the Roanne Hospital’s ED Layout, proving efficacy and scalability. This study underlines the importance of intelligent layout planning in improving emergency department performance and healthcare resilience during future pandemics.