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<p>This study explores patient flow logistics (PFL) from strategic and tactical viewpoints, introducing a decision-making framework to systematically categorize key challenges. Through a systematic literature review, the research analyzes articles published between 2010 and 2023 in 17 top-tier journals within Operations Research (OR) and Management Science (MS), using SCOPUS and healthcare-specific keywords (e.g., “Capacity Planning,” “Resource Allocation,” “Pandemic,” “Hospital,” “Patient”) to retrieve relevant studies. After applying publication year, journal quality, and optimization technique filters—including mathematical programming and stochastic optimization—66 articles were selected for analysis. Findings reveal an increasing reliance on stochastic models, greater adoption of metaheuristic over heuristic algorithms, and stronger integration of real-world data in decision-making frameworks. Nevertheless, gaps persist, particularly in maximization-based methods addressing service rates and equity in epidemic resource allocation. The proposed classification framework offers actionable insights for academics, policymakers, and healthcare professionals by distinguishing pandemic and non-pandemic logistics. Future research should explore cross-sector hospital cooperation, integrated patient management strategies, and the incorporation of disease spread models in predictive resource planning.</p>

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Optimizing patient flow logistics: strategic challenges, tactical solutions, and future directions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月21日

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  • Hamed Zamani,
  • Fereshteh Parvaresh,
  • Mehdi Nasr Isfahani

摘要

This study explores patient flow logistics (PFL) from strategic and tactical viewpoints, introducing a decision-making framework to systematically categorize key challenges. Through a systematic literature review, the research analyzes articles published between 2010 and 2023 in 17 top-tier journals within Operations Research (OR) and Management Science (MS), using SCOPUS and healthcare-specific keywords (e.g., “Capacity Planning,” “Resource Allocation,” “Pandemic,” “Hospital,” “Patient”) to retrieve relevant studies. After applying publication year, journal quality, and optimization technique filters—including mathematical programming and stochastic optimization—66 articles were selected for analysis. Findings reveal an increasing reliance on stochastic models, greater adoption of metaheuristic over heuristic algorithms, and stronger integration of real-world data in decision-making frameworks. Nevertheless, gaps persist, particularly in maximization-based methods addressing service rates and equity in epidemic resource allocation. The proposed classification framework offers actionable insights for academics, policymakers, and healthcare professionals by distinguishing pandemic and non-pandemic logistics. Future research should explore cross-sector hospital cooperation, integrated patient management strategies, and the incorporation of disease spread models in predictive resource planning.