Nurse scheduling problem

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The nurse scheduling problem (NSP), also called the nurse rostering problem (NRP), is the operations research problem of finding an optimal way to assign nurses to shifts, typically with a set of hard constraints which all valid solutions must follow, and a set of soft constraints which define the relative quality of valid solutions. [1] Solutions to the nurse scheduling problem can be applied to constrained scheduling problems in other fields. [2] [3]

Contents

While research on computer-assisted employee scheduling goes back to the 1950s, [4] the nurse scheduling problem in its current form was introduced in two parallel publications in 1976. [5] [6] It is known to have NP-hard complexity. [1]

General description

The nurse scheduling problem involves the assignment of shifts and holidays to nurses. Each nurse has their own wishes and restrictions, as does the hospital. The problem is described as finding a schedule that both respects the constraints of the nurses and fulfills the objectives of the hospital. Conventionally, a nurse can work 3 shifts because nursing is shift work:

In this problem we must search for a solution satisfying as many wishes as possible while not compromising the needs of the hospital.

Constraints

There are two types of constraints:

Some examples of constraints are:

Hard constraints typically include a specification of shifts (e.g. morning, afternoon, and night), that each nurse should work no more than one shift per day, and that all patients should have nursing coverage. [1] Differences in qualifications between nurses also create hard constraints. [7] Soft constraints may include minimum and maximum numbers of shifts assigned to a given nurse in a given week, of hours worked per week, of days worked consecutively, of days off consecutively, and so on. [1] The shift preferences of individual nurses may be treated as a soft constraint, [8] or as a hard constraint. [9]

Solutions

Solutions to the problem use a variety of techniques, including both mathematically exact solutions [8] and a variety of heuristic solutions using decomposition, [10] parallel computing, [10] [11] stochastic optimization, [1] genetic algorithms, [8] colony optimization, [8] simulated annealing, [8] quantum annealing [12] Tabu search, [8] and coordinate descent. [11] [13]

Burke et al. (2004) [14] summarised the state of art of academic research to the nurse rostering problem, including brief introductions of various then published solutions.

See also

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References

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  2. Aickelin, Uwe; Dowsland, Kathryn A. (2004). "An Indirect Genetic Algorithm for a Nurse Scheduling Problem". Computers & Operations Research. 31 (5): 761–778. arXiv: 0803.2969 . doi:10.1016/s0305-0548(03)00034-0. S2CID   8772185.
  3. Beddoe, Gareth; Petrovic, Sanja (2003). "A novel approach to finding feasible solutions to personnel rostering problems" (PDF). Savannah, Georgia: Proceedings of the 14th Annual Conference of the Production and Operation Management Society: 1–13. Retrieved 20 March 2014.{{cite journal}}: Cite journal requires |journal= (help)
  4. Bailey, Norman T. J. (1956). "Statistics in Hospital Planning and Design". Journal of the Royal Statistical Society Series C: Applied Statistics. 5 (3). Oxford University Press: 146–157. doi:10.2307/2985416. JSTOR   2985416 . Retrieved 14 December 2023.
  5. Miller, Holmes E.; Pierskalla, William P.; Rath, Gustave J. (1976). "Nurse Scheduling Using Mathematical Programming". Operations Research. 24 (5). INFORMS: 857–870. doi:10.1287/opre.24.5.857 . Retrieved 14 December 2023.
  6. Warner, D. Michael (1976). "Scheduling Nursing Personnel According to Nursing Preference: A Mathematical Programming Approach". Operations Research. 24 (5). INFORMS: 842–856. doi:10.1287/opre.24.5.842 . Retrieved 14 December 2023.
  7. Aickelin, Uwe; White, Paul (2004). "Building Better Nurse Scheduling Algorithms". Annals of Operations Research . 128 (1–4): 159–177. arXiv: 0803.2967 . doi:10.1023/b:anor.0000019103.31340.a6. S2CID   14983974.
  8. 1 2 3 4 5 6 Goodman, Melissa D.; Dowsland, Kathryn A.; Thompson, Jonathan M. (2007). "A grasp-knapsack hybrid for a nurse-scheduling problem" (PDF). Journal of Heuristics. 15 (4). Springer: 351–379. doi:10.1007/s10732-007-9066-7. S2CID   8784023 . Retrieved 20 June 2020.
  9. Winstanley, Graham. "A hybrid approach to staff scheduling: The Staff Work Allocation Tool (SWAT)" (PDF). Brighton: University of Brighton School of Computing, Engineering and Mathematics: 1–12. Archived from the original (PDF) on 20 March 2014. Retrieved 20 March 2014.{{cite journal}}: Cite journal requires |journal= (help)
  10. 1 2 Lagatie, Ruben; Haspeslagh, Stefaan; De Causmaecker, Patrick (2009). "Negotiation Protocols for Distributed Nurse Rostering" (PDF). Eindhoven University of Technology Department of Computer Science. Archived from the original (PDF) on 4 March 2016. Retrieved 14 February 2014.{{cite journal}}: Cite journal requires |journal= (help)
  11. 1 2 Bäumelt, Zdeněk; Dvořák, Jan; Šůcha, Přemysl; Hanzálek, Zdeněk (2016). "A Novel Approach for Nurse Rerostering based on a Parallel Algorithm". European Journal of Operational Research . 251 (2). Elsevier: 624–639. doi:10.1016/j.ejor.2015.11.022.
  12. Humble, Travis S.; Nakamura, Yuma; Ikeda, Kazuki (2019-04-27). "Application of Quantum Annealing to Nurse Scheduling Problem". Scientific Reports. 9 (1): 12837. arXiv: 1904.12139 . Bibcode:2019NatSR...912837I. doi:10.1038/s41598-019-49172-3. PMC   6731278 . PMID   31492936.
  13. Augustine, Lizzy; Faer, Morgan; Kavountzis, Andreas; Patel, Reema (15 December 2009). "A Brief Study of the Nurse Scheduling Problem (NSP)" (PDF). Pittsburgh: Carnegie Mellon School of Computer Science: 1–11. Retrieved 20 March 2014.{{cite journal}}: Cite journal requires |journal= (help)
  14. Burke, Edmund; De Causmaecker, Patrick; Berghe, Greet Vanden; Van Landeghem, Hendrik (2004). "The state of the art of nurse rostering". Journal of Scheduling. 7 (6): 441–499. doi:10.1023/B:JOSH.0000046076.75950.0b. S2CID   10537343 . Retrieved 10 January 2016.