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通过多级学习和列生成优化手术室安排:一种新的混合方法
Rong Zhao1, Yaqin Quan1, Guangrui Fan2
1Department of Anesthesiology, Shanxi Provincial People Hospital, 99 Shuangta East Street, Taiyuan, Shanxi, 030001, China.
Health care management science
|September 27, 2025
概括
本研究引入了用于手术室 (OR) 调度,提高效率和患者护理的混合框架. 这种新的方法提高了OR利用率,并通过先进的优化技术减少了患者等待时间.
科学领域:
- 医疗保健 运营 研究 研究 研究
- 人工智能在医学中的应用
- 医院管理系统 医院管理系统
背景情况:
- 手术室 (OR) 的安排对于患者的治疗结果和医院的效率至关重要.
- 传统的调度方法面临着复杂的约束和不确定性的挑战.
- 优化OR计划需要平衡多个目标和现实世界的变化.
研究的目的:
- 开发一种新的混合框架,以优化手术室 (OR) 调度.
- 将多级优化与强化学习和列生成集成在一起.
- 解决OR调度中的复杂约束和不确定性,以提高效率.
主要方法:
- 一个混合框架将问题分解成战略,战术和操作等级.
- 整合强化学习以指导列生成以获得增强的调度选项.
- 整合强大的不确定性处理机制,以适应可变的手术持续时间和资源可用性.
主要成果:
- 平均患者等待时间减少15.8% (10.1至8.5天).
- 增加了5.4个百分点的OR利用率 (73.8%至79.2%).
- 在不确定性条件下实现了92.5%的可行性率,并将计划中断减少了26.2%.
结论:
- 混合框架为优化OR调度提供了一个实用且可扩展的解决方案.
- 在真正的医院环境中,在医疗保健提供和运营绩效方面取得了显著的改善.
- 为医院效率和患者护理的可持续改进提供了一种可行的方法.
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