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Robust and data-driven approaches to call centers

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  • Bertsimas, Dimitris
  • Doan, Xuan Vinh

Abstract

We propose both robust and data-driven approaches to a fluid model of call centers that incorporates random arrival rates with abandonment to determine staff levels and dynamic routing policies. We test the resulting models with real data obtained from the call center of a US bank. Computational results show that the robust fluid model is significantly more tractable as compared to the data-driven one and produces overall better solutions to call centers in most experiments.

Suggested Citation

  • Bertsimas, Dimitris & Doan, Xuan Vinh, 2010. "Robust and data-driven approaches to call centers," European Journal of Operational Research, Elsevier, vol. 207(2), pages 1072-1085, December.
    • Handle: RePEc:eee:ejores:v:207:y:2010:i:2:p:1072-1085
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    References listed on IDEAS

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    1. Noah Gans & Ger Koole & Avishai Mandelbaum, 2003. "Telephone Call Centers: Tutorial, Review, and Research Prospects," Manufacturing & Service Operations Management, INFORMS, vol. 5(2), pages 79-141, September.
    2. Achal Bassamboo & Assaf Zeevi, 2009. "On a Data-Driven Method for Staffing Large Call Centers," Operations Research, INFORMS, vol. 57(3), pages 714-726, June.
    3. J. Michael Harrison & Assaf Zeevi, 2005. "A Method for Staffing Large Call Centers Based on Stochastic Fluid Models," Manufacturing & Service Operations Management, INFORMS, vol. 7(1), pages 20-36, September.
    4. Lawrence Brown & Noah Gans & Avishai Mandelbaum & Anat Sakov & Haipeng Shen & Sergey Zeltyn & Linda Zhao, 2005. "Statistical Analysis of a Telephone Call Center: A Queueing-Science Perspective," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 36-50, March.
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    Citations

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    Cited by:

    1. Gabrel, Virginie & Murat, Cécile & Thiele, Aurélie, 2014. "Recent advances in robust optimization: An overview," European Journal of Operational Research, Elsevier, vol. 235(3), pages 471-483.
    2. Li, Dongmin & Hu, Qingpei & Wang, Lujia & Yu, Dan, 2019. "Statistical inference for Mt/G/Infinity queueing systems under incomplete observations," European Journal of Operational Research, Elsevier, vol. 279(3), pages 882-901.
    3. Schwarz, Justus Arne & Selinka, Gregor & Stolletz, Raik, 2016. "Performance analysis of time-dependent queueing systems: Survey and classification," Omega, Elsevier, vol. 63(C), pages 170-189.
    4. Yanbo Ma & Kaiyue Liu & Zheng Li & Xiang Chen, 2022. "Robust Operating Room Scheduling Model with Violation Probability Consideration under Uncertain Surgery Duration," IJERPH, MDPI, vol. 19(20), pages 1-20, October.
    5. Selim Mankaï, 2014. "Data-Driven Robust Optimization with Application to Portfolio Management," Working Papers 2014-104, Department of Research, Ipag Business School.
    6. Selim Mankaï & Khaled Guesmi, 2015. "Robust Portfolio Protection: A Scenarios-based Approach," Bankers, Markets & Investors, ESKA Publishing, issue 138, pages 30-44, September.
    7. repec:ipg:wpaper:2014-394 is not listed on IDEAS
    8. Ying Chen & John J. Hasenbein, 2017. "Staffing large-scale service systems with distributional uncertainty," Queueing Systems: Theory and Applications, Springer, vol. 87(1), pages 55-79, October.
    9. Defraeye, Mieke & Van Nieuwenhuyse, Inneke, 2016. "Staffing and scheduling under nonstationary demand for service: A literature review," Omega, Elsevier, vol. 58(C), pages 4-25.
    10. Özgün Turgut & Florian Taube & Stefan Minner, 2018. "Data-driven retail inventory management with backroom effect," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(4), pages 945-968, October.
    11. 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.
    12. Selim Mankai & Khaled Guesmi, 2014. "Robust Portfolio Protection: A Scenarios-Based Approach," Working Papers hal-04141326, HAL.
    13. Tsiligianni, Christiana & Tsiligiannis, Aristeides & Tsiliyannis, Christos, 2023. "A stochastic inventory model of COVID-19 and robust, real-time identification of carriers at large and infection rate via asymptotic laws," European Journal of Operational Research, Elsevier, vol. 304(1), pages 42-56.
    14. Gregor Selinka & Raik Stolletz & Thomas I. Maindl, 2022. "Performance Approximation for Time-Dependent Queues with Generally Distributed Abandonments," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 20-38, January.

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