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Parametric Forecasting and Stochastic Programming Models for Call-Center Workforce Scheduling

Author

Listed:
  • Noah Gans

    (Operations, Information and Decisions Department, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Haipeng Shen

    (Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599; and Innovation and Information Management, School of Business, University of Hong Kong, Pok Fu Lam, Hong Kong)

  • Yong-Pin Zhou

    (Department of Information Systems and Operations Management, The Michael G. Foster School of Business, University of Washington, Seattle, Washington 98195)

  • Nikolay Korolev

    (Genesys Telecommunications Laboratories, Daly City, California 94014)

  • Alan McCord

    (Genesys Telecommunications Laboratories, Daly City, California 94014)

  • Herbert Ristock

    (Genesys Telecommunications Laboratories, Daly City, California 94014)

Abstract

We develop and test an integrated forecasting and stochastic programming approach to workforce management in call centers. We first demonstrate that parametric forecasts, discretized using Gaussian quadrature, can be used to drive stochastic programs whose results are stable with relatively small numbers of scenarios. We then extend our approach to include forecast updates and two-stage stochastic programs with recourse and provide a general modeling framework for which recent, related models are special cases. In our formulations, the inclusion of multiple arrival-rate scenarios allows call centers to meet long-run average quality-of-service targets, and the use of recourse actions helps them to lower long-run average costs. Experiments with two large sets of call-center data highlight the complementary nature of these elements.

Suggested Citation

  • Noah Gans & Haipeng Shen & Yong-Pin Zhou & Nikolay Korolev & Alan McCord & Herbert Ristock, 2015. "Parametric Forecasting and Stochastic Programming Models for Call-Center Workforce Scheduling," Manufacturing & Service Operations Management, INFORMS, vol. 17(4), pages 571-588, October.
    • Handle: RePEc:inm:ormsom:v:17:y:2015:i:4:p:571-588
    DOI: 10.1287/msom.2015.0546
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    References listed on IDEAS

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    Cited by:

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    3. Ta, Thuy Anh & Chan, Wyean & Bastin, Fabian & L’Ecuyer, Pierre, 2021. "A simulation-based decomposition approach for two-stage staffing optimization in call centers under arrival rate uncertainty," European Journal of Operational Research, Elsevier, vol. 293(3), pages 966-979.
    4. Kuang Xu & Carri W. Chan, 2016. "Using Future Information to Reduce Waiting Times in the Emergency Department via Diversion," Manufacturing & Service Operations Management, INFORMS, vol. 18(3), pages 314-331, July.
    5. Perla, Abhinav & Nikolaev, Alexander & Pasiliao, Eduardo, 2018. "Workforce management under social Link Based Corruption," Omega, Elsevier, vol. 78(C), pages 222-236.
    6. Masoud Kamalahmadi & Qiuping Yu & Yong-Pin Zhou, 2021. "Call to Duty: Just-in-Time Scheduling in a Restaurant Chain," Management Science, INFORMS, vol. 67(11), pages 6751-6781, November.
    7. Ibrahim, Rouba & Ye, Han & L’Ecuyer, Pierre & Shen, Haipeng, 2016. "Modeling and forecasting call center arrivals: A literature survey and a case study," International Journal of Forecasting, Elsevier, vol. 32(3), pages 865-874.
    8. Barış Ata & Xiaoshan Peng, 2020. "An Optimal Callback Policy for General Arrival Processes: A Pathwise Analysis," Operations Research, INFORMS, vol. 68(2), pages 327-347, March.
    9. Notz, Pascal M. & Wolf, Peter K. & Pibernik, Richard, 2023. "Prescriptive analytics for a multi-shift staffing problem," European Journal of Operational Research, Elsevier, vol. 305(2), pages 887-901.

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