考虑公平和护士偏好的护士排班研究
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摘要
针对护士排班问题涉及护士满意度的特点,在护士排班过程中加入护士偏好和公平的约束,寻求最优的排班表以增加护士的满意度。根据多目标问题的特点,采用粒子群多目标优化算法。在硬约束条件上,加入N班之后不能上A班和P班的约束,使护士在上N班之后能够得到足够的休息。在算法设计上,加入变异算子,扩大了粒子群的搜索空间。由于各优化目标之间存在一定的矛盾,用多目标决策理论可以更加科学客观地优化护士排班表。在最后的案例分析中,发现护士不同的偏好会产生不同的非劣解,因此在实际排班中,要充分考虑护士的偏好,以求出更加科学合理的排班表。
Considering that the issue of nurses scheduling problem involves the satisfaction of the nurse, this paper joins nurses' preferences and fair constraints into the nurses scheduling, to find the best schedule to increase the satisfaction of nurses. According to the characteristics of multi-objective problem, this paper uses multi-objective particle swarm optimization algorithm. In the hard constraints, a night shift can only be followed by a day off or a night shift to ensure enough rest for nurses before the next working shift. In the algorithm design, this paper adds mutation operator to expand the particle swarm search space. Due to the existence of some contradictory between the optimization objectives, using multiobjective decision theory can be more scientific and objective optimization of nurses scheduling table. In the final case analysis, it is found that different preferences of nurses will produce different non-inferior solutions. Therefore, in practical scheduling, the nurses' preferences must be taken into full consideration in order to find a more scientific and reasonable schedule.
Considering that the issue of nurses scheduling problem involves the satisfaction of the nurse, this paper joins nurses' preferences and fair constraints into the nurses scheduling, to find the best schedule to increase the satisfaction of nurses. According to the characteristics of multi-objective problem, this paper uses multi-objective particle swarm optimization algorithm. In the hard constraints, a night shift can only be followed by a day off or a night shift to ensure enough rest for nurses before the next working shift. In the algorithm design, this paper adds mutation operator to expand the particle swarm search space. Due to the existence of some contradictory between the optimization objectives, using multiobjective decision theory can be more scientific and objective optimization of nurses scheduling table. In the final case analysis, it is found that different preferences of nurses will produce different non-inferior solutions. Therefore, in practical scheduling, the nurses' preferences must be taken into full consideration in order to find a more scientific and reasonable schedule.
引文
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[13]Altamirano L,Riff M C,Araya I,et al.Anesthesiology nurse scheduling using particle swarm optimization[J].International Journal of Computational Intelligence Systems,2012,5(1):111-125.
[14]Wong T C,Xu M,Chin K S.A two-stage heuristic approach for nurse scheduling problem:a case study in an emergency department[J].Computers&Operations Research,2014,51(51):99-110.
[15]Wu T H,Yeh J Y,Lee Y M.A particle swarm optimization approach with refinement procedure for nurse rostering problem[J].Computers&Operations Research,2015,54(C):52-63.
[16]张爽,马红梅,沈向英.APN排班模式对临床护理工作影响的Meta分析[J].中国护理管理,2014,14(8):834-838.
[17]Lin C W,Yang L,Fournier-Viger P,et al.A binary PSOapproach to mine high-utility itemsets[J].Soft Computing,2017,21(17):5103-5121.
[18]郁磊.MATLAB智能算法30个案例分析[M].2版.北京:北京航空航天大学出版社,2015.
[2]闫亚敏,张薇,龚梅.护理冲突管理与护士离职率的相关性研究进展[J].护理管理杂志,2012,12(2):112-114.
[3]Sarucan A.Applicability of artificial bee colony algorithm for nurse scheduling problems[J].International Journal of Computational Intelligence Systems,2014,7(S1):121-136.
[4]彭黄莉,牛占文.基于目标规划的连续性排班问题研究[J].武汉理工大学学报(信息与管理工程版),2013,35(5):718-722.
[5]Huang H,Lin W,Lin Z,et al.An evolutionary algorithm based on constraint set partitioning for nurse rostering problems[J].Neural Computing&Applications,2014,25(3/4):703-715.
[6]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.
[7]欧阳骥,林伟佳,卓晓燕,等.基于整数规划与演化优化混合的护士排班问题求解算法[J].计算机应用研究,2015,32(12):3660-3664.
[8]王超,董兴业.求解护士排班问题的变邻域搜索算法[J].计算机应用,2013,33(2):338-352.
[9]Niu B,Liu J,Bi Y,et al.Improved bacterial foraging optimization algorithm with information communication mechanism[C]//10th International Conference on Computational Intelligence and Security,2014:47-51.
[10]Yilmaz E.A mathematical programming model for scheduling of nurses’labor shifts[J].Journal of Medical Systems,2012,36(2):491-496.
[11]Jafari H,Bateni S,Daneshvar P,et al.Fuzzy mathematical modeling approach for the nurse scheduling problem:a case study[J].International Journal of Fuzzy Systems,2016,18(2):320-332.
[12]Shahnazari-Shahrezaei P,Tavakkoli-Moghaddam R,Azarkish M,et al.A differential evolution algorithm developed for a nurse scheduling problem[J].South African Journal of Industrial Engineering,2012,23(3):68-90.
[13]Altamirano L,Riff M C,Araya I,et al.Anesthesiology nurse scheduling using particle swarm optimization[J].International Journal of Computational Intelligence Systems,2012,5(1):111-125.
[14]Wong T C,Xu M,Chin K S.A two-stage heuristic approach for nurse scheduling problem:a case study in an emergency department[J].Computers&Operations Research,2014,51(51):99-110.
[15]Wu T H,Yeh J Y,Lee Y M.A particle swarm optimization approach with refinement procedure for nurse rostering problem[J].Computers&Operations Research,2015,54(C):52-63.
[16]张爽,马红梅,沈向英.APN排班模式对临床护理工作影响的Meta分析[J].中国护理管理,2014,14(8):834-838.
[17]Lin C W,Yang L,Fournier-Viger P,et al.A binary PSOapproach to mine high-utility itemsets[J].Soft Computing,2017,21(17):5103-5121.
[18]郁磊.MATLAB智能算法30个案例分析[M].2版.北京:北京航空航天大学出版社,2015.