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Online Scheduling Policies for Multiclass Call Centers with Impatient Customers

Author

Listed:
  • Oualid Jouini

    () (Pôle de Recherche - Rouen Business School - Rouen Business School)

  • Auke Pot

    (EMLYON Business school - EMLYON Business School)

  • Ger Koole
  • Yves Dallery

Abstract

We consider a call center with two classes of impatient customers: premium and regular classes. Modeling our call center as a multiclass GI=GI=s þ M queue, we focus on developing scheduling policies that satisfy a target ratio constraint on the abandonment probabilities of premium customers to regular ones. The problem is inspired by a real call center application in which we want to reach some predefined preference between customer classes for any workload condition. The motivation for this constraint comes from the difficulty of predicting in a quite satisfying way the workload. In such a case, the traditional routing problem formulation with differentiated service levels for different customer classes would be useless. For this new problem formulation, we propose two families of online scheduling policies: queue joining and call selection policies. The principle of our policies is that we adjust their routing rules by dynamically changing their parameters. We then evaluate the performance of these policies through a numerical study. The policies are characterized by simplicity and ease of implementation.

Suggested Citation

  • Oualid Jouini & Auke Pot & Ger Koole & Yves Dallery, 2010. "Online Scheduling Policies for Multiclass Call Centers with Impatient Customers," Post-Print hal-00565528, HAL.
  • Handle: RePEc:hal:journl:hal-00565528
    DOI: 10.1016/j.ejor.2010.02.036
    Note: View the original document on HAL open archive server: https://hal-rbs.archives-ouvertes.fr/hal-00565528
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    Cited by:

    1. repec:eee:ejores:v:274:y:2019:i:1:p:303-316 is not listed on IDEAS
    2. Terry James & Kevin Glazebrook & Kyle Lin, 2016. "Developing Effective Service Policies for Multiclass Queues with Abandonment: Asymptotic Optimality and Approximate Policy Improvement," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 251-264, May.
    3. 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.
    4. Urtzi Ayesta & Peter Jacko & Vladimir Novak, 2017. "Scheduling of multi-class multi-server queueing systems with abandonments," Journal of Scheduling, Springer, vol. 20(2), pages 129-145, April.

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