Xue Jun, Li Zhi, Zhang Shuangli
Department of General Surgery, Hebei Provincial Key Laboratory of Systems Biology and Gene Regulation, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, China.
School of Economics and Management, Tiangong University, Tianjin, 300387, China.
Sci Rep. 2025 Jan 31;15(1):3946. doi: 10.1038/s41598-025-87867-y.
This paper focuses on the elective surgical scheduling problem with multi-resource constraints, including material resources, such as operating rooms (ORs) and non-operating room (NOR) beds, and human resources (i.e., surgeons, anesthesiologists, and nurses). The objective of multi-resource constrained elective surgical scheduling (MESS) is to simultaneously minimize the average recovery completion time for all patients, the average overtime for medical staffs, and the total medical cost. This problem can be formulated as a mixed integer linear multi-objective optimization model, and the honey badger algorithm based on the Nash equilibrium (HBA-NE) is developed for the MESS. Experimental studies were carried out to test the performance of the proposed approach, and the performance of the proposed surgical scheduling scheme was validated. Finally, to narrow the gap between the optimal surgical scheduling solution and actual hospital operations, digital twin (DT) technology is adopted to build a physical-virtual hospital surgery simulation model. The experimental results show that by introducing a digital twin, the physical and virtual spaces of the smart hospital can be integrated to visually simulate and verify surgical processes.
本文聚焦于具有多资源约束的择期手术调度问题，这些资源约束包括物质资源，如手术室（OR）和非手术室（NOR）床位，以及人力资源（即外科医生、麻醉师和护士）。多资源约束择期手术调度（MESS）的目标是同时最小化所有患者的平均康复完成时间、医护人员的平均加班时间以及总医疗成本。该问题可被表述为一个混合整数线性多目标优化模型，并且基于纳什均衡的蜜獾算法（HBA-NE）被开发用于解决MESS。开展了实验研究以测试所提方法的性能，并验证了所提手术调度方案的性能。最后，为缩小最优手术调度解决方案与实际医院运营之间的差距，采用数字孪生（DT）技术构建了一个物理-虚拟医院手术模拟模型。实验结果表明，通过引入数字孪生，智能医院的物理空间和虚拟空间可以整合起来，以直观地模拟和验证手术过程。
