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Two-stage workforce planning under demand fluctuations and uncertainty

Author

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  • X Zhu

    (Virginia Polytechnic Institute and State University)

  • H D Sherali

    (Virginia Polytechnic Institute and State University)

Abstract

We address a multi-category workforce planning problem for functional areas located at different service centres, each having office-space and recruitment capacity constraints, and facing fluctuating and uncertain workforce demand. A deterministic model is initially developed to deal with workforce fluctuations based on an expected demand profile over the horizon. To hedge against the demand uncertainty, we also propose a two-stage stochastic program, in which the first stage makes personnel recruiting and allocation decisions, while the second stage reassigns workforce demand among all units. A Benders’ decomposition-based algorithm is designed to solve this two-stage stochastic mixed-integer program. Computational results based on some practical numerical experiments are presented to provide insights on applying the deterministic versus the stochastic programming approach, and to demonstrate the efficacy of the proposed algorithm as compared with directly solving the model using its deterministic equivalent.

Suggested Citation

  • X Zhu & H D Sherali, 2009. "Two-stage workforce planning under demand fluctuations and uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 94-103, January.
    • Handle: RePEc:pal:jorsoc:v:60:y:2009:i:1:d:10.1057_palgrave.jors.2602522
    DOI: 10.1057/palgrave.jors.2602522
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    References listed on IDEAS

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    Cited by:

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    3. Douglas S. Altner & Anthony C. Rojas & Leslie D. Servi, 2018. "A two-stage stochastic program for multi-shift, multi-analyst, workforce optimization with multiple on-call options," Journal of Scheduling, Springer, vol. 21(5), pages 517-531, October.
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    7. Fernandes, Rui & Gouveia, Borges & Pinho, Carlos, 2013. "A real options approach to labour shifts planning under different service level targets," European Journal of Operational Research, Elsevier, vol. 231(1), pages 182-189.
    8. Zhenzhen Yan & Sarah Yini Gao & Chung Piaw Teo, 2018. "On the Design of Sparse but Efficient Structures in Operations," Management Science, INFORMS, vol. 64(7), pages 3421-3445, July.
    9. Restrepo, María I. & Gendron, Bernard & Rousseau, Louis-Martin, 2017. "A two-stage stochastic programming approach for multi-activity tour scheduling," European Journal of Operational Research, Elsevier, vol. 262(2), pages 620-635.
    10. Valeva, Silviya & Hewitt, Mike & Thomas, Barrett W. & Brown, Kenneth G., 2017. "Balancing flexibility and inventory in workforce planning with learning," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 194-207.
    11. Corominas, Albert & Lusa, Amaia & Olivella, Jordi, 2012. "A detailed workforce planning model including non-linear dependence of capacity on the size of the staff and cash management," European Journal of Operational Research, Elsevier, vol. 216(2), pages 445-458.
    12. Tohidi, Mohammad & Kazemi Zanjani, Masoumeh & Contreras, Ivan, 2021. "A physician planning framework for polyclinics under uncertainty," Omega, Elsevier, vol. 101(C).
    13. 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.
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