面向择期患者的多医院手术室联合排程研究

Multi-hospital Operating Room Scheduling of Elective Patients

多医院手术室的联合排程能够有效缓解医疗服务压力，促进医疗资源配置优化，提高医疗运作效率。现有的手术排程研究多集中于一家医院内部手术资源的优化，极少考虑多医院参与的多手术室联合排程。以生产调度理论为基础，把多家医院看作多个工厂，把手术和术后麻醉恢复看作两阶段加工过程，以最小化最大手术完成时间为目标函数，建立了分布式两阶段混合流水车间调度模型，提出了基于自适应学习的遗传算法，在传统遗传算法中引入包含自我学习、社会学习和随机学习的三段式学习操作，并通过多组测试问题对算法性能进行了评价和对比分析，验证了算法的有效性。

The elective surgery scheduling problem involves operating rooms from different hospitals. Multi-hospital operating room scheduling is capable of reducing the burden of medical service,optimizing the allocation of medical resources and improving the efficiency of medical treatment. By regarding the hospitals as factories, the perioperative stage and the postoperative stage as a two-stage process, the distributed two-stage no-wait hybrid flow-shop problem model was established and the objective is to minimize the maximum operation time. An Adaptive Learning-based Genetic Algorithm（ALBGA）is used to solve the problem and experiment results validate its effectiveness.