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<p>Nowadays, managing Operating Rooms (ORs) in healthcare systems presents a significant challenge, with underutilization of surgical facilities being a key concern. Several decision factors must be considered, including the number of ORs, operating hours, target utilization rates, and the possibility of patient transfers between surgical units (SUs). This study presents a multi-criteria mixed-integer linear optimization model designed for the specific context of Policlinico Umberto I in Rome. The model uses an open scheduling approach to determine the optimal day, time, and OR allocation for weekly surgeries. Using real data from two SUs and four ORs over twelve weeks, we assess the impact of different operating time policies on hospital Key Performance Indicators. Additionally, we introduce randomly generated data to simulate surgery priorities and further validate our model. Results demonstrate the effectiveness of mathematical optimization in improving OR utilization and overall healthcare efficiency.</p>

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Multi-criteria Optimization Scheduling of Surgical Units: A Case Study at Policlinico Umberto I of Rome

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月20日

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  • Anna Livia Croella,
  • Edoardo Maria Tronci,
  • Alberto Nastasi,
  • Laura Palagi,
  • Federico Petitti,
  • Ferdinando Romano

摘要

Nowadays, managing Operating Rooms (ORs) in healthcare systems presents a significant challenge, with underutilization of surgical facilities being a key concern. Several decision factors must be considered, including the number of ORs, operating hours, target utilization rates, and the possibility of patient transfers between surgical units (SUs). This study presents a multi-criteria mixed-integer linear optimization model designed for the specific context of Policlinico Umberto I in Rome. The model uses an open scheduling approach to determine the optimal day, time, and OR allocation for weekly surgeries. Using real data from two SUs and four ORs over twelve weeks, we assess the impact of different operating time policies on hospital Key Performance Indicators. Additionally, we introduce randomly generated data to simulate surgery priorities and further validate our model. Results demonstrate the effectiveness of mathematical optimization in improving OR utilization and overall healthcare efficiency.