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

Elin Bjørk Bodvarsdottir, Niels-Christian Fink Bagger, Laura Elise Høffner, Thomas Jacob Riis Stidsen

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Researchers have studied the nurse rostering problem (NRP) 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 struc-ture 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.
Original languageEnglish
JournalJournal of Scheduling
ISSN1094-6136
Publication statusSubmitted - 2021

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