Skip to main content
SpringerLink
Log in
Book cover

International Conference on Machine Learning, Optimization, and Data Science

LOD 2018: Machine Learning, Optimization, and Data Science pp 460–471Cite as

Lookahead Policy and Genetic Algorithm for Solving Nurse Rostering Problems

  • Conference paper
  • First Online:

  • 2124 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11331))

Abstract

Previous research has shown that value function approximation in dynamic programming does not perform too well when tackling difficult combinatorial optimisation problems such as multi-stage nurse rostering. This is because the large action space that needs to be explored. This paper proposes to replace the value function approximation with a genetic algorithm in order to generate solutions for the dynamic programming stages. Then, the paper proposes a hybrid approach that generates sets of weekly rosters with a genetic algorithm for consideration by the lookahead procedure that assembles a solution for the whole planning horizon of several weeks. Results indicate that this hybrid between a genetic algorithm and the lookahead policy mechanism from dynamic programming exhibits a more competitive performance than the value function approximation dynamic programming investigated before. Results also show that the proposed algorithm ranks well in respect of several other algorithms applied to the same set of problem instances. The intended contribution of this paper is towards a better understanding of how to successfully apply dynamic programming mechanisms to tackle difficult combinatorial optimisation problems.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Powell, W.B.: Approximate Dynamic Programming: Solving the Curses of Dimensionality, vol. 703. Wiley, New York (2007)

    Book  Google Scholar 

  2. Van den Bergh, J., Beliën, J., De Bruecker, P., Demeulemeester, E., De Boeck, L.: Personnel scheduling: a literature review. Eur. J. Oper. Res. 226(3), 367–385 (2013)

    Article  MathSciNet  Google Scholar 

  3. Burke, E.K., De Causmaecker, P., Berghe, G.V., Van Landeghem, H.: The state of the art of nurse rostering. J. Sched. 7(6), 441–499 (2004)

    Article  MathSciNet  Google Scholar 

  4. Shi, P., Landa-Silva, D.: Dynamic programming with approximation function for nurse scheduling. In: Pardalos, P.M., Conca, P., Giuffrida, G., Nicosia, G. (eds.) MOD 2016. LNCS, vol. 10122, pp. 269–280. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-51469-7_23

    Chapter  Google Scholar 

  5. Davis, S.G., Reutzel, E.T.: A dynamic programming approach to work force scheduling with time-dependent performance measures. J. Oper. Manag. 1(3), 165–171 (1981)

    Article  Google Scholar 

  6. Maenhout, B., Vanhoucke, M.: NSPLib - a nurse scheduling problem library: a tool to evaluate (meta-)heuristic procedures. In: O.R. in Health, pp. 151–165. Elsevier (2005)

    Google Scholar 

  7. Shi, P., Landa-Silva, D.: Approximate dynamic programming with combined policy functions for solving multi-stage nurse rostering problem. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R. (eds.) MOD 2017. LNCS, vol. 10710, pp. 349–361. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-72926-8_29

    Chapter  Google Scholar 

  8. Back, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, Oxford (1996)

    MATH  Google Scholar 

  9. Yagiura, M., Ibaraki, T.: The use of dynamic programming in genetic algorithms for permutation problems. Eur. J. Oper. Res. 92(2), 387–401 (1996)

    Article  Google Scholar 

  10. Mohammadi, M., Forghani, K.: A hybrid method based on genetic algorithm and dynamic programming for solving a bi-objective cell formation problem considering alternative process routings and machine duplication. Appl. Soft Comput. 53, 97–110 (2017)

    Article  Google Scholar 

  11. Ceschia, S., Dang, N.T.T., De Causmaecker, P., Haspeslagh, S., Schaerf, A.: Second international nurse rostering competition (INRC-II)–problem description and rules–. arXiv preprint arXiv:1501.04177 (2015)

  12. INRC-II the second nurse rostering competition. http://mobiz.vives.be/inrc2/. Accessed 23 May 2016

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, ASAP Research Group, University of Nottingham, Nottingham, UK

    Peng Shi & Dario Landa-Silva

Authors
  1. Peng Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dario Landa-Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peng Shi .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy and University of Reading, Reading, UK

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. University of Catania, Catania, Italy

    Giovanni Giuffrida

  4. Harvard University, Cambridge, MA, USA

    Renato Umeton

  5. IBM, Tivoli Research Lab, Rome, Italy

    Vincenzo Sciacca

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shi, P., Landa-Silva, D. (2019). Lookahead Policy and Genetic Algorithm for Solving Nurse Rostering Problems. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2018. Lecture Notes in Computer Science(), vol 11331. Springer, Cham. https://doi.org/10.1007/978-3-030-13709-0_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-13709-0_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-13708-3

  • Online ISBN: 978-3-030-13709-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation