IDEAS home Printed from https://ideas.repec.org/a/kap/hcarem/v26y2023i1d10.1007_s10729-022-09613-4.html
   My bibliography  Save this article

Nurse rostering with fatigue modelling

Author

Listed:
  • Kjartan Kastet Klyve

    (Norwegian University of Science and Technology)

  • Ilankaikone Senthooran

    (Monash University)

  • Mark Wallace

    (Monash University)

Abstract

We use a real Nurse Rostering Problem and a validated model of human sleep to formulate the Nurse Rostering Problem with Fatigue. The fatigue modelling includes individual biologies, thus enabling personalised schedules for every nurse. We create an approximation of the sleep model in the form of a look-up table, enabling its incorporation into nurse rostering. The problem is solved using an algorithm that combines Mixed-Integer Programming and Constraint Programming with a Large Neighbourhood Search. A post-processing algorithm deals with errors, to produce feasible rosters minimising global fatigue. The results demonstrate the realism of protecting nurses from highly fatiguing schedules and ensuring the alertness of staff. We further demonstrate how minimally increased staffing levels enable lower fatigue, and find evidence to suggest biological complementarity among staff can be used to reduce fatigue. We also demonstrate how tailoring shifts to nurses’ biology reduces the overall fatigue of the team, which means managers must grapple with the issue of fairness in rostering.

Suggested Citation

  • Kjartan Kastet Klyve & Ilankaikone Senthooran & Mark Wallace, 2023. "Nurse rostering with fatigue modelling," Health Care Management Science, Springer, vol. 26(1), pages 21-45, March.
    • Handle: RePEc:kap:hcarem:v:26:y:2023:i:1:d:10.1007_s10729-022-09613-4
    DOI: 10.1007/s10729-022-09613-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10729-022-09613-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10729-022-09613-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Xu, Shuling & Hall, Nicholas G., 2021. "Fatigue, personnel scheduling and operations: Review and research opportunities," European Journal of Operational Research, Elsevier, vol. 295(3), pages 807-822.
    2. Sanja Petrovic & Greet Berghe, 2012. "A comparison of two approaches to nurse rostering problems," Annals of Operations Research, Springer, vol. 194(1), pages 365-384, April.
    3. Asvin Goel & Thibaut Vidal, 2014. "Hours of Service Regulations in Road Freight Transport: An Optimization-Based International Assessment," Transportation Science, INFORMS, vol. 48(3), pages 391-412, August.
    4. Florian Mischek & Nysret Musliu, 2019. "Integer programming model extensions for a multi-stage nurse rostering problem," Annals of Operations Research, Springer, vol. 275(1), pages 123-143, April.
    5. Ernst, A. T. & Jiang, H. & Krishnamoorthy, M. & Sier, D., 2004. "Staff scheduling and rostering: A review of applications, methods and models," European Journal of Operational Research, Elsevier, vol. 153(1), pages 3-27, February.
    6. Michael Ingre & Wessel Van Leeuwen & Tomas Klemets & Christer Ullvetter & Stephen Hough & Göran Kecklund & David Karlsson & Torbjörn Åkerstedt, 2014. "Validating and Extending the Three Process Model of Alertness in Airline Operations," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-15, October.
    7. Elina Rönnberg & Torbjörn Larsson, 2010. "Automating the self-scheduling process of nurses in Swedish healthcare: a pilot study," Health Care Management Science, Springer, vol. 13(1), pages 35-53, March.
    8. Frederik Knust & Lin Xie, 2019. "Simulated annealing approach to nurse rostering benchmark and real-world instances," Annals of Operations Research, Springer, vol. 272(1), pages 187-216, January.
    9. Schoenfelder, Jan & Bretthauer, Kurt M. & Wright, P. Daniel & Coe, Edwin, 2020. "Nurse scheduling with quick-response methods: Improving hospital performance, nurse workload, and patient experience," European Journal of Operational Research, Elsevier, vol. 283(1), pages 390-403.
    10. Ta-Chung Wang & Cheng-Che Liu, 2014. "Optimal Work Shift Scheduling with Fatigue Minimization and Day Off Preferences," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-8, April.
    11. Fügener, Andreas & Pahr, Alexander & Brunner, Jens O., 2018. "Mid-term nurse rostering considering cross-training effects," International Journal of Production Economics, Elsevier, vol. 196(C), pages 176-187.
    12. Burke, Edmund K. & Curtois, Timothy & Post, Gerhard & Qu, Rong & Veltman, Bart, 2008. "A hybrid heuristic ordering and variable neighbourhood search for the nurse rostering problem," European Journal of Operational Research, Elsevier, vol. 188(2), pages 330-341, July.
    13. Elín Björk Böðvarsdóttir & Niels-Christian Fink Bagger & Laura Elise Høffner & Thomas J. R. Stidsen, 2022. "A flexible mixed integer programming-based system for real-world nurse rostering," Journal of Scheduling, Springer, vol. 25(1), pages 59-88, February.
    14. Dohn, Anders & Mason, Andrew, 2013. "Branch-and-price for staff rostering: An efficient implementation using generic programming and nested column generation," European Journal of Operational Research, Elsevier, vol. 230(1), pages 157-169.
    15. Stefaan Haspeslagh & Patrick De Causmaecker & Andrea Schaerf & Martin Stølevik, 2014. "The first international nurse rostering competition 2010," Annals of Operations Research, Springer, vol. 218(1), pages 221-236, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Emir Demirović & Nysret Musliu & Felix Winter, 2019. "Modeling and solving staff scheduling with partial weighted maxSAT," Annals of Operations Research, Springer, vol. 275(1), pages 79-99, April.
    2. Elín Björk Böðvarsdóttir & Niels-Christian Fink Bagger & Laura Elise Høffner & Thomas J. R. Stidsen, 2022. "A flexible mixed integer programming-based system for real-world nurse rostering," Journal of Scheduling, Springer, vol. 25(1), pages 59-88, February.
    3. Van den Bergh, Jorne & Beliën, Jeroen & De Bruecker, Philippe & Demeulemeester, Erik & De Boeck, Liesje, 2013. "Personnel scheduling: A literature review," European Journal of Operational Research, Elsevier, vol. 226(3), pages 367-385.
    4. Toni I. Wickert & Alberto F. Kummer Neto & Márcio M. Boniatti & Luciana S. Buriol, 2021. "An integer programming approach for the physician rostering problem," Annals of Operations Research, Springer, vol. 302(2), pages 363-390, July.
    5. Vanhoucke, Mario & Maenhout, Broos, 2009. "On the characterization and generation of nurse scheduling problem instances," European Journal of Operational Research, Elsevier, vol. 196(2), pages 457-467, July.
    6. Sara Ceschia & Rosita Guido & Andrea Schaerf, 2020. "Solving the static INRC-II nurse rostering problem by simulated annealing based on large neighborhoods," Annals of Operations Research, Springer, vol. 288(1), pages 95-113, May.
    7. Wolbeck, Lena Antonia, 2019. "Fairness aspects in personnel scheduling," Discussion Papers 2019/16, Free University Berlin, School of Business & Economics.
    8. Paola Cappanera & Filippo Visintin & Roberta Rossi, 2022. "The emergency department physician rostering problem: obtaining equitable solutions via network optimization," Flexible Services and Manufacturing Journal, Springer, vol. 34(4), pages 916-959, December.
    9. Topaloglu, Seyda, 2009. "A shift scheduling model for employees with different seniority levels and an application in healthcare," European Journal of Operational Research, Elsevier, vol. 198(3), pages 943-957, November.
    10. Asvin Goel & Thibaut Vidal & Adrianus Leendert Kok, 2021. "To team up or not: single versus team driving in European road freight transport," Flexible Services and Manufacturing Journal, Springer, vol. 33(4), pages 879-913, December.
    11. Burke, Edmund K. & Li, Jingpeng & Qu, Rong, 2010. "A hybrid model of integer programming and variable neighbourhood search for highly-constrained nurse rostering problems," European Journal of Operational Research, Elsevier, vol. 203(2), pages 484-493, June.
    12. Burke, Edmund K. & Curtois, Tim, 2014. "New approaches to nurse rostering benchmark instances," European Journal of Operational Research, Elsevier, vol. 237(1), pages 71-81.
    13. Florian Mischek & Nysret Musliu, 2019. "Integer programming model extensions for a multi-stage nurse rostering problem," Annals of Operations Research, Springer, vol. 275(1), pages 123-143, April.
    14. Frederik Knust & Lin Xie, 2019. "Simulated annealing approach to nurse rostering benchmark and real-world instances," Annals of Operations Research, Springer, vol. 272(1), pages 187-216, January.
    15. Sanja Petrovic, 2019. "“You have to get wet to learn how to swim” applied to bridging the gap between research into personnel scheduling and its implementation in practice," Annals of Operations Research, Springer, vol. 275(1), pages 161-179, April.
    16. Rahimian, Erfan & Akartunalı, Kerem & Levine, John, 2017. "A hybrid Integer Programming and Variable Neighbourhood Search algorithm to solve Nurse Rostering Problems," European Journal of Operational Research, Elsevier, vol. 258(2), pages 411-423.
    17. Glass, Celia A. & Knight, Roger A., 2010. "The nurse rostering problem: A critical appraisal of the problem structure," European Journal of Operational Research, Elsevier, vol. 202(2), pages 379-389, April.
    18. Emir Hüseyin Özder & Evrencan Özcan & Tamer Eren, 2019. "Staff Task-Based Shift Scheduling Solution with an ANP and Goal Programming Method in a Natural Gas Combined Cycle Power Plant," Mathematics, MDPI, vol. 7(2), pages 1-26, February.
    19. Ademir Constantino & Dario Landa-Silva & Everton Melo & Candido Mendonça & Douglas Rizzato & Wesley Romão, 2014. "A heuristic algorithm based on multi-assignment procedures for nurse scheduling," Annals of Operations Research, Springer, vol. 218(1), pages 165-183, July.
    20. Lai, David S.W. & Leung, Janny M.Y. & Dullaert, Wout & Marques, Inês, 2020. "A graph-based formulation for the shift rostering problem," European Journal of Operational Research, Elsevier, vol. 284(1), pages 285-300.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:hcarem:v:26:y:2023:i:1:d:10.1007_s10729-022-09613-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.