Abstract: With the prevalence of laminar flow operating rooms in hospitals, scheduling for such operating rooms hasbecome a critical concern in hospital operations management. Compared to conventional operatingrooms, laminar flow operating rooms consume a significant amount of energy while providing a cleaner, more comfortable, and safer surgical environment for patients. Faced with the challenge of high energy consumption in laminar flow operating rooms, hospitals often implement technological, managerial, and behavioral energy-saving measures. The generation of energy consumption is primarily driven by the demand for medical activities. As a pivotal department in hospitals, operating rooms involve extensive medical activities and costs. Therefore, from the perspective of energy-efficient management, this paper proposes research on the green scheduling problem of laminar flow surgery centers, focusing on optimizing scheduling to assist hospitals in providing higher-quality surgical medical services to patients with reduced energy consumption and lower costs.
In the context of pursuing environmental sustainability andlow-carbon initiatives, this paper proposes research on thegreen scheduling of laminar flow surgery centers, considering the interests of multiple stakeholders, including patients, hospitals, and society. Firstly, we employ “perceived preoperative wait time” as a metric for patient satisfaction to improve the preoperative waiting process, and address the most frequent patient complaints. Secondly, we use “laminar flow operating center usage duration” as an indicator for hospital surgical system operations to offer patients services with fewer overtime hours, reduced costs, and increased efficiency. Finally, we take “carbonemissions” as a green indicator to reflect the hospital's green initiatives and social responsibility.
For the multi-objective laminar flow surgery green scheduling problem, this study develops a reentrant laminar flow surgery green scheduling model with objectives including patient preoperative waiting time, surgery center utilization time, and carbon emissions. A hybrid improved optimization algorithm (INSGAII-LS) is proposed for solving this problem. The algorithm introduces innovative cooperative search strategies, population initialization policies, variable-scale crossover and mutation strategies, as well as a depth-first search iteration strategy within local search, enhancing the search capability of the solution space. Additionally, considering the characteristics of the problem, a data-driven decoding strategy is designed and its effectiveness is verified. The study conducts numerical experiments and simulation tests at different scales, comparing the performance and stability of the proposed algorithm to other effective algorithms (IMSSA, IMOGWO, NSGA-II). The simulation results indicate that the key factor determining the duration of surgical center utilization duration is patient prioritization. However, reducing the duration of the laminar flow operating center does not directly lead to a decrease in carbon emissions. It is essential to consider the cumulative carbon emissions generated by the operating room. Consequently, scheduling operating rooms is crucial in surgical planning. The research outcomes can provide valuable insights and decision references for the multi-objective optimization in green scheduling of the laminar flow operating center.
It should be noted that the proposed decoding strategy in this paper relies on reliable data prediction. In the future, the author'steam will conduct research focused on predicting service duration around the laminar flow operating center.This will involve exploring reentrant laminar flow green scheduling under the uncertainty of surgicalduration and studying distributed surgical green scheduling within the context of internet healthcare.
These efforts aim to provide new theoretical foundations, methodological insights, and decision references for the green operation of laminar flow operating centers.

Key words: laminar flow surgical scheduling, reentrant, multi-objective, green scheduling, NSGA-II algorithm

摘要： 针对考虑患者、医院、社会多方利益下的可重入层流手术绿色调度问题,提出了混合优化算法INSGAII-LS以同时最小化三个目标:平均患者术前等待感知时长、层流手术中心使用时长和层流手术中心碳排放。首先,算法创新设计了合作搜索策略、种群初始化策略、变尺度交叉与变异策略,LS深度搜索迭代策略,以增强解空间的搜索能力;然后,根据问题特点设计了数据驱动解码策略,并通过三种解码策略对比实验,验证了本文提出的解码策略的有效性;最后,通过不同规模数值实验和仿真案例测试了本文算法相比于其他有效算法(IMSSA,IMOGWO,NSGA-II)的优越性和稳定性。案例仿真发现,决定手术中心使用时长的关键因素是对患者的排序;而手术中心使用时长的缩短并不直接导致碳排放量的减少,还需关注手术室的累积碳排放量。因此,层流手术室的调度在层流手术规划中至关重要。研究结果可为层流手术绿色调度多目标优化提供方法借鉴和决策参考。

关键词: 层流手术调度, 可重入, 多目标, 绿色调度, NSGA-II算法