AI 中文总结
本研究针对公立医院手术等待问题，提出两阶段手术室分配框架，结合混合整数线性规划模型与优先级患者分配流程，经模拟验证其可均衡手术时间分配、降低等待时间且计算效率更高。
AI 中文摘要
手术室分配是公立医院面临的重大挑战，其外科容量有限且择期手术等待名单庞大。本研究针对专科区块调度环境提出一种两阶段手术室分配框架，该方法将用于医学专科区块分配的混合整数线性规划模型与基于优先级的患者分配流程相结合。通过使用从历史手术及等待名单数据估算的参数，在一周及多周的模拟场景中对该框架进行评估。在包含患者确认失败、手术暂停等现实运营干扰的情况下，将所提方法与从文献改编的集成混合整数线性规划基线进行对比。结果显示，所提框架在各专科间提供的手术时间与需求的分配更均衡，同时保持较高的手术室利用率，且计算时间远低于集成基线。在20周场景中，所提方法实现了更短的等待时间，在维持运营灵活性的同时收治了更多患者。这些结果表明，基于分解的手术室分配策略为降低公立医院手术等待名单提供了一种实用且计算高效的替代方案。
英文摘要
Operating-room allocation is a major challenge for public hospitals with limited surgical capacity and large elective waiting lists. This work proposes a two-stage operating-room allocation framework for specialty-block scheduling environments. The methodology combines a mixed-integer linear programming model for medical specialty block allocation with a priority-based patient allocation procedure. The framework was evaluated through one-week and multi-week simulation scenarios using parameters estimated from historical surgical and waiting-list data. The proposed methodology was compared against an integrated mixed-integer linear programming baseline adapted from the literature under realistic operational disruptions, including failed patient confirmations and surgery suspensions. Results show that the proposed framework achieves a more balanced distribution between offered and demanded surgical time across specialties while maintaining high operating-room utilization and substantially lower computational times than the integrated baseline. In the 20-week scenario, the proposed methodology achieved lower waiting times and operated a larger number of patients while preserving operational flexibility. These results suggest that decomposition-based operating-room allocation strategies provide a practical and computationally efficient alternative for reducing surgical waiting lists in public hospitals.
Comments26 pages, 7 figures, 8 tables
