基于演化算法的多级别多岗位护士排班问题研究
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- 英文论文题名：Multi-level Nurse Scheduling with Multi-level Nursing Positions Based on Evolutionary Algorithm
- 论文作者：李加加 ; 沈吟东
- 英文论文作者：Jiajia Li ; Yindong Shen ; School of Automation ; Huazhong University of Science and Technology ; Image Processing and Intelligent Control Key Laboratory of Education Ministry
- 年：2017
- 作者机构：华中科技大学自动化学院图像信息处理与智能控制教育部重点实验室;
- 论文关键词：护士排班 ; 多级别护士 ; 护理岗位 ; 护士偏好 ; 演化算法
- 英文论文关键词：Nurse scheduling ; Multi-level nurses ; Nursing positions ; Nurse preference ; Evolutionary algorithm
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：中文
- 分类号：R47;TP18
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：493k
- 原文格式：D
- 会议级别：全国
摘要
护士排班是医院必不可少的重要工作之一,其优劣直接影响着医院的护理成本和护理质量。目前我国仍普遍采用手工排班方式,缺乏对多级别和多岗位护士排班问题的研究,针对此并结合我国护士排班的实际习惯与偏好,本文建立了多级别多岗位的护士排班目标规划模型,并设计出一个基于演化算法的护士排班方法(简称ENS)。该ENS方法首先根据需要护理的病人数生成一个可以满足护理需求的初始解,然后利用演化算法对初始解进行迭代优化。一个演化迭代过程包括:解的分解、演化摧毁、随机重构以及解的验收四个步骤。ENS方法在演化摧毁阶段嵌入了模仿适者生存的机制,以提高解的性能;在随机重构阶段提出四种不同的排序策略,进一步加快重构速度,保证重构质量。实验表明,ENS方法能有效求解带个人偏好的多级别多岗位护士排班问题.
Nurse scheduling plays an important role in hospital operations. An efficient schedule may affect considerably the operating cost and nursing quality of a hospital. Unfortunately, nurse schedules are compiled manually in China and the research is lacking in considering nurse scheduling with multi-level nurses and multi-level nursing positions. Considering the real-world routines and preferences in China, this paper proposes a goal programming model for the multi-level nurse scheduling problem with multi-level nursing positions. To solve the model, an evolutionary algorithm based nursing scheduling approach(ENS for short) is devised. The ENS approach compiles first an initial solution based on nursing needs determined by the number of patients, then refines the solution using an evolutionary algorithm iteratively. Each iteration contains four operations upon a solution: decomposition, evolutionary destruction, randomly rebuild and acceptance check. The evolutionary process is carried out iteratively until the certain ending condition is met. In the stage of evolutionary destruction, the mechanism of the survival of the fittest is embedded to improve the solution quality. In the stage of randomly rebuild, four different scheduling strategies are proposed to further speed up the rebuild speed and ensure the quality of reconstruction. Experiments show that ENS can effectively compile schedules with lower cost and higher preference satisfaction than manual schedules.
Nurse scheduling plays an important role in hospital operations. An efficient schedule may affect considerably the operating cost and nursing quality of a hospital. Unfortunately, nurse schedules are compiled manually in China and the research is lacking in considering nurse scheduling with multi-level nurses and multi-level nursing positions. Considering the real-world routines and preferences in China, this paper proposes a goal programming model for the multi-level nurse scheduling problem with multi-level nursing positions. To solve the model, an evolutionary algorithm based nursing scheduling approach(ENS for short) is devised. The ENS approach compiles first an initial solution based on nursing needs determined by the number of patients, then refines the solution using an evolutionary algorithm iteratively. Each iteration contains four operations upon a solution: decomposition, evolutionary destruction, randomly rebuild and acceptance check. The evolutionary process is carried out iteratively until the certain ending condition is met. In the stage of evolutionary destruction, the mechanism of the survival of the fittest is embedded to improve the solution quality. In the stage of randomly rebuild, four different scheduling strategies are proposed to further speed up the rebuild speed and ensure the quality of reconstruction. Experiments show that ENS can effectively compile schedules with lower cost and higher preference satisfaction than manual schedules.
引文
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[9]沈吟东,陈名晖,邓婕.利用矩阵向量化变换求解护士排班问题[A].中国控制与决策会议论文集[C].沈阳:东北大学出版社,2008,1019-1022.
[10]Edmund K.Burke,Patrick De Causmaecker,Sanja Petrovic,et al.Metaheuristics for handling time interval coverage constraints in nurse scheduling[J].Applied Artificial Intelligence,2006,20(9):743-766.
[11]Ohki M,Morimoto A,Miyake K.Nurse Scheduling by Using Cooperative GA with Efficient Mutation and Mountain-Climbing Operators[C]//International IEEE Conference on Intelligent Systems.IEEE,2006:164-169.
[12]Li J,Aickelin U.A bayesian optimization algorithm for the nurse scheduling problem[J].Computer Science,2003,3(1):2149-2156.
[13]Burke E K,Li J,Qu R.A hybrid model of integer programming and variable neighbourhood search for highly-constrained nurse rostering problems[J].European Journal of Operational Research,2010,203(2):484-493.
[14]沈吟东,苏光辉.带约束的护士排班模型和基于变换规则的优化算法[J].计算机工程与科学,2010,32(7):99-103.
[15]苏光辉.护士需求预测及优化排班问题研究[D].武汉:华中科技大学,2009,24-36.
[2]阎崇钧,唐加福,姜博文,等.考虑患者选择和公平性的序列预约调度方法[J].系统工程学报,2014,29(1):104-112.
[3]王昱,唐加福.医院手术调度问题的两阶段鲁棒优化方法研究[J].系统工程学报,2016,31(4):431-440.
[4]Aickelin U,Li J.An estimation of distribution algorithm for nurse scheduling[J].Annals of Operations Research,2007,155(1):289-309.
[5]Bard J F,Purnomo H W.Cyclic preference scheduling of nurses using a Lagrangian-based heuristic[J].Journal of Scheduling,2007,10(1):5-23.
[6]Weil G,Heus K,Francois P,et al.Constraint programming for nurse scheduling[J].IEEE Engineering in Medicine&Biology Magazine,1995,14(4):417-422.
[7]Jaumard B,Semet F,Vovor T.A generalized linear programming model for nurse scheduling[J].European Journal of Operational Research,1998,107(1):1-18.
[8]Bard J F,Purnomo H W.A column generation-based approach to solve the preference scheduling problem for nurses with downgrading[J].Socio-Economic Planning Sciences,2005,39(3):193-213.
[9]沈吟东,陈名晖,邓婕.利用矩阵向量化变换求解护士排班问题[A].中国控制与决策会议论文集[C].沈阳:东北大学出版社,2008,1019-1022.
[10]Edmund K.Burke,Patrick De Causmaecker,Sanja Petrovic,et al.Metaheuristics for handling time interval coverage constraints in nurse scheduling[J].Applied Artificial Intelligence,2006,20(9):743-766.
[11]Ohki M,Morimoto A,Miyake K.Nurse Scheduling by Using Cooperative GA with Efficient Mutation and Mountain-Climbing Operators[C]//International IEEE Conference on Intelligent Systems.IEEE,2006:164-169.
[12]Li J,Aickelin U.A bayesian optimization algorithm for the nurse scheduling problem[J].Computer Science,2003,3(1):2149-2156.
[13]Burke E K,Li J,Qu R.A hybrid model of integer programming and variable neighbourhood search for highly-constrained nurse rostering problems[J].European Journal of Operational Research,2010,203(2):484-493.
[14]沈吟东,苏光辉.带约束的护士排班模型和基于变换规则的优化算法[J].计算机工程与科学,2010,32(7):99-103.
[15]苏光辉.护士需求预测及优化排班问题研究[D].武汉:华中科技大学,2009,24-36.