A flexible mixed integer programming-based system for real-world nurse rostering

Elín Björk Böðvarsdóttir (), Niels-Christian Fink Bagger, Laura Elise Høffner and Thomas J. R. Stidsen
Additional contact information
Elín Björk Böðvarsdóttir: Technical University of Denmark
Niels-Christian Fink Bagger: Technical University of Denmark
Laura Elise Høffner: Region Zealand
Thomas J. R. Stidsen: Technical University of Denmark

Journal of Scheduling, 2022, vol. 25, issue 1, No 4, 59-88

Abstract: Abstract Researchers have studied the nurse rostering problem for multiple decades. Initially, the formulations were rather primitive including only a few necessary restrictions, but down the road, the formulations have become more complex. Nonetheless, a fraction of the research reaches implementation in practice, and many wards still schedule nurses manually. In this article, we introduce a flexible nurse rostering system that employs mathematical optimization to automatically schedule nurses to shifts. We have developed this system in collaboration with practitioners to fully match their needs. The system consists of a comprehensive mixed integer programming (MIP) model along with a flexible framework. In addition to common constraints from the literature, the mathematical formulation includes three new constraints that further encourage healthy work schedules for each nurse. Additionally, we have reformulated some common constraints from the literature and allow for a complex shift structure that matches the needs of real hospital wards. This flexibility results in increased adaptability for different wards with different needs and is crucial to address the complex nurse rostering problem that practitioners face. We have successfully implemented this system in two wards at two Danish hospitals. We present the MIP model along with computational results for 12 real-world rostering instances. Furthermore, we discuss the practical impact of this system and provide general feedback from the practitioners using it. Overall, the results illustrate the capabilities of the system to tackle diverse nurse rostering instances and produce outstanding results.

Keywords: Nurse rostering; Automatic scheduling; Mixed integer programming; Preference scheduling; OR applications in healthcare (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10951-021-00705-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jsched:v:25:y:2022:i:1:d:10.1007_s10951-021-00705-7

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10951

DOI: 10.1007/s10951-021-00705-7

Access Statistics for this article

Journal of Scheduling is currently edited by Edmund Burke and Michael Pinedo

More articles in Journal of Scheduling from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().