Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan.
Artificial Intelligence for Operations Management Research Center, National Taiwan University of Science and Technology, Taipei, Taiwan.
J Med Syst. 2024 Aug 12;48(1):75. doi: 10.1007/s10916-024-02092-w.
The nurse scheduling problem (NSP) has been a crucial and challenging research issue for hospitals, especially considering the serious deterioration in nursing shortages in recent years owing to long working hours, considerable work pressure, and irregular lifestyle, which are important in the service industry. This study investigates the NSP that aims to maximize nurse satisfaction with the generated schedule subject to government laws, internal regulations of hospitals, doctor-nurse pairing rules, shift and day off preferences of nurses, etc. The computational experiment results show that our proposed hybrid metaheuristic outperforms other metaheuristics and manual scheduling in terms of both computation time and solution quality. The presented solution procedure is implemented in a real-world clinic, which is used as a case study. The developed scheduling technique reduced the time spent on scheduling by 93% and increased the satisfaction of the schedule by 21%, which further enhanced the operating efficiency and service quality.
护士排班问题（NSP）一直是医院的一个关键且具有挑战性的研究问题，尤其是考虑到近年来由于工作时间长、工作压力大以及生活不规律等因素，护理人员严重短缺，这些因素在服务行业中很重要。本研究调查了护士排班问题（NSP），旨在生成的排班方案中最大化护士的满意度，同时要考虑政府法规、医院内部规定、医生-护士配对规则、护士班次和休息日偏好等因素。计算实验结果表明，我们提出的混合元启发式算法在计算时间和解决方案质量方面均优于其他元启发式算法和手动排班。所提出的解决方案在一个实际的诊所中实施，并将其作为案例研究。开发的排班技术将排班时间缩短了 93%，并将排班满意度提高了 21%，从而进一步提高了运营效率和服务质量。
