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Intelligent Clinic Nurse Scheduling Considering Nurses Paired with Doctors and Preference of Nurses.
Tsao, Yu-Chung; Chen, Danny; Hwang, Feng-Jang; Linh, Vu Thuy.
Affiliation
  • Tsao YC; Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan. yctsao@mail.ntust.edu.tw.
  • Chen D; Artificial Intelligence for Operations Management Research Center, National Taiwan University of Science and Technology, Taipei, Taiwan. yctsao@mail.ntust.edu.tw.
  • Hwang FJ; Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan.
  • Linh VT; Artificial Intelligence for Operations Management Research Center, National Taiwan University of Science and Technology, Taipei, Taiwan.
J Med Syst ; 48(1): 75, 2024 Aug 12.
Article in En | MEDLINE | ID: mdl-39133348
ABSTRACT
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.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Personnel Staffing and Scheduling / Job Satisfaction Limits: Humans Language: En Journal: J Med Syst Year: 2024 Document type: Article Affiliation country: Country of publication:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Personnel Staffing and Scheduling / Job Satisfaction Limits: Humans Language: En Journal: J Med Syst Year: 2024 Document type: Article Affiliation country: Country of publication: