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A two-stage stochastic program for scheduling and allocating cross-trained workers

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  • G M Campbell

    (Fairfield University)

Abstract

A two-stage stochastic program is developed for scheduling and allocating cross-trained workers in a multi-department service environment with random demands. The first stage corresponds to scheduling days-off over a time horizon such as a week or month. The second stage is the recourse action that deals with allocating available workers at the beginning of a day to accommodate realized demands. After the general two-stage model is formulated, a special case is introduced for computational testing. The testing helps quantify the value of cross-training as a function of problem characteristics. Results show that cross-training can be more valuable than perfect information, especially when demand uncertainty is high.

Suggested Citation

  • G M Campbell, 2011. "A two-stage stochastic program for scheduling and allocating cross-trained workers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1038-1047, June.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:6:d:10.1057_jors.2010.16
    DOI: 10.1057/jors.2010.16
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    References listed on IDEAS

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    Cited by:

    1. Dipankar Bose & A. K. Chatterjee, 2015. "Specialized versus Multi-skilled Workforce: A Newsboy Approach for Call Centre Resource Planning," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 40(3-4), pages 252-267, August.
    2. 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.
    3. Tohidi, Mohammad & Kazemi Zanjani, Masoumeh & Contreras, Ivan, 2021. "A physician planning framework for polyclinics under uncertainty," Omega, Elsevier, vol. 101(C).
    4. Restrepo, María I. & Rousseau, Louis-Martin & Vallée, Jonathan, 2020. "Home healthcare integrated staffing and scheduling," Omega, Elsevier, vol. 95(C).
    5. 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.
    6. Brusco, Michael J., 2015. "A bicriterion algorithm for the allocation of cross-trained workers based on operational and human resource objectives," European Journal of Operational Research, Elsevier, vol. 247(1), pages 46-59.
    7. De Bruecker, Philippe & Van den Bergh, Jorne & Beliën, Jeroen & Demeulemeester, Erik, 2015. "Workforce planning incorporating skills: State of the art," European Journal of Operational Research, Elsevier, vol. 243(1), pages 1-16.
    8. 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.
    9. Kayse Lee Maass & Boying Liu & Mark S. Daskin & Mary Duck & Zhehui Wang & Rama Mwenesi & Hannah Schapiro, 2017. "Incorporating nurse absenteeism into staffing with demand uncertainty," Health Care Management Science, Springer, vol. 20(1), pages 141-155, March.
    10. Gang Li & Joy M. Field & Hongxun Jiang & Tian He & Youming Pang, 2019. "Decision Models for Workforce and Technology Planning in Services," Papers 1909.12829, arXiv.org.
    11. Gnanlet, Adelina & Gilland, Wendell G., 2014. "Impact of productivity on cross-training configurations and optimal staffing decisions in hospitals," European Journal of Operational Research, Elsevier, vol. 238(1), pages 254-269.

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