Assigning nurses to appropriate departments and work shifts based on human factors can strengthen teamwork and boost the efficiency of healthcare systems. The human factors considered in this study include skill, preference, and compatibility of nurses. In this regard, a unique multi-objective mathematical model for nurse scheduling is proposed in this article, in which nurses' decision-making styles are taken into account. Three objectives, including minimization of the total cost of staffing, minimization of the sum of incompatibility among nurses' decision-making styles assigned to the same shift days, and maximization of the overall satisfaction of nurses for their assigned shifts, are addressed in this model. Three meta-heuristics, namely, multi-objective Keshtel algorithm, non-dominated sorting genetic algorithm II, and multi-objective tabu search, are developed to solve the problem. Moreover, a data envelopment analysis method is employed to rank the obtained Pareto solutions. Afterwards, a real-life case at a large hospital in Tehran, Iran, is investigated. Eventually, the applicability and effectiveness of the proposed model are assessed based on the experimental results.

中文翻译：

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通过强调人为因素的护士调度问题的多目标模型。

根据人为因素将护士分配到适当的部门和工作班次可以加强团队合作并提高医疗保健系统的效率。本研究中考虑的人为因素包括护士的技能，偏爱和兼容性。为此，本文提出了一种独特的护士调度多目标数学模型，其中考虑了护士的决策风格。此模型解决了三个目标，包括最小化人员配置总成本，最小化分配给同一轮班日的护士决策风格之间的不相容性总和，最大程度地提高了护士对其分配的轮班的总体满意度。三种元启发式算法，即多目标Keshtel算法，非支配排序遗传算法II，并开发了多目标禁忌搜索来解决该问题。此外，采用数据包络分析方法对获得的帕累托解进行排序。之后，调查了伊朗德黑兰一家大型医院的真实案例。最终，基于实验结果评估了所提出模型的适用性和有效性。