摘要
在医院管理中,护士排班是一项复杂且关键的任务,对提升护理质量和医院运营效率至关重要。传统排班方法依赖人工经验,存在班次分配不均、连续工作时间过长等问题,难以满足现代医院的需求。文章采用Python语言,结合粒子群优化(Particle Swarm Optimization,PSO)和遗传算法(Genetic Algorithm,GA),对多目标护士排班优化模型进行迭代求解。通过将两者结合,能够充分利用两者的优点,克服单一算法的不足。将该算法应用于县级医院骨科科室的护士排班,结果显示,与传统的人工排班相比,智能排班模式在多个方面表现出显著优势。
In hospital management,nurse scheduling is a complex and crucial task,which is of great significance for improving the quality of nursing care and the operational efficiency of hospitals.Traditional scheduling methods rely on manual experience and have problems such as uneven shift allocation and excessively long consecutive working hours,making it difficult to meet the needs of modern hospitals.In the paper,the Python language is used,combined with the particle swarm optimization(PSO)algorithm and the genetic algorithm(GA),to iteratively solve the multi-objective nurse scheduling optimization model.By combining the two algorithms,their respective advantages can be fully utilized,and the deficiencies of a single algorithm can be overcome.When this algorithm is applied to the nurse scheduling in the orthopedic department of a county-level hospital,the results show that,compared with the traditional manual scheduling,the intelligent scheduling mode demonstrates significant advantages in multiple aspects.
作者
马瑞国
易校石
MA Ruiguo;YI Xiaoshi(Institute of Applied Mathematics,School of Mathematics and Statistics,Yili Normal University,Yining Xinjiang 835000,China)
出处
《信息与电脑》
2025年第9期58-60,共3页
Information & Computer
基金
伊犁师范大学创新创业训练计划“护士排班智能优化算法支持”(项目编号:202310764001)。
关键词
机器学习
护士排班管理
粒子群优化算法
遗传算法
骨科
machine learning
nurse scheduling management
particle swarm optimization algorithm
genetic algorithm
orthopedics
