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Robust shift generation in workforce planning

Author

Listed:
  • Dori Hulst

    (Blue Rock Logistics)

  • Dick Hertog

    (Tilburg University)

  • Wim Nuijten

    (Quintiq
    Eindhoven University of Technology)

Abstract

In this paper we apply robust optimization techniques to the shift generation problem in workforce planning. At the time that the shifts are generated, there is often much uncertainty in the workload predictions. We propose a model to generate shifts that are robust against this uncertainty. An adversarial approach is used to solve the resulting robust optimization model. In each iteration an integer nonlinear knapsack problem is solved to calculate the worst case workload scenario. We apply the approach to generate shifts in a real-life Air Traffic Controller workforce planning problem. The numerical results show the value of our approach.

Suggested Citation

  • Dori Hulst & Dick Hertog & Wim Nuijten, 2017. "Robust shift generation in workforce planning," Computational Management Science, Springer, vol. 14(1), pages 115-134, January.
    • Handle: RePEc:spr:comgts:v:14:y:2017:i:1:d:10.1007_s10287-016-0265-2
    DOI: 10.1007/s10287-016-0265-2
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    References listed on IDEAS

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    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.
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    7. Bretthauer, Kurt M. & Shetty, Bala, 2002. "The nonlinear knapsack problem - algorithms and applications," European Journal of Operational Research, Elsevier, vol. 138(3), pages 459-472, May.
    8. Duan Li & Xiaoling Sun, 2006. "Nonlinear Integer Programming," International Series in Operations Research and Management Science, Springer, number 978-0-387-32995-6, December.
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    Cited by:

    1. Black, Ben & Ainslie, Russell & Dokka, Trivikram & Kirkbride, Christopher, 2023. "Distributionally robust resource planning under binomial demand intakes," European Journal of Operational Research, Elsevier, vol. 306(1), pages 227-242.
    2. Pieter Smet & Annelies Lejon & Greet Vanden Berghe, 2021. "Demand smoothing in shift design," Flexible Services and Manufacturing Journal, Springer, vol. 33(2), pages 457-484, June.
    3. Wu, Zhiying & Xu, Guoning & Chen, Qingxin & Mao, Ning, 2023. "Two stochastic optimization methods for shift design with uncertain demand," Omega, Elsevier, vol. 115(C).

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