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Dynamic Routing Policies for Multi-Skill Call Centers Using Deep Q Network

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  • Qin Zhang

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

Abstract

When the call center queuing system becomes complex, it turns out that the static routing policy is not optimal. This paper considers the problem of the dynamic routing policy for call centers with multiple skill types and agent groups. A state-dependent routing policy based on the Deep Q Network (DQN) is proposed, and a reinforcement learning algorithm is applied to optimize the routing. A simulation algorithm is designed to help customers and agents interact with the external environment to learn the optimal strategy. The performance evaluation considered in this paper is the service level/abandon rate. Experiments show that the DQN-based dynamic routing policy performs better than the common static policy Global First Come First Serve (FCFS) and the dynamic policy Priorities with Idle Agent Thresholds and Weight-Based Routing in various examples. On the other hand, the training time of the routing policy model based on the DQN is much faster than routing optimization based on simulation and a genetic algorithm.

Suggested Citation

  • Qin Zhang, 2023. "Dynamic Routing Policies for Multi-Skill Call Centers Using Deep Q Network," Mathematics, MDPI, vol. 11(22), pages 1-18, November.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:22:p:4662-:d:1281461
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    References listed on IDEAS

    as
    1. Benjamin Legros, 2018. "Waiting time based routing policies to parallel queues with percentiles objectives," Post-Print hal-02065918, HAL.
    2. Mehmet Tolga Cezik & Pierre L'Ecuyer, 2008. "Staffing Multiskill Call Centers via Linear Programming and Simulation," Management Science, INFORMS, vol. 54(2), pages 310-323, February.
    3. John N. Tsitsiklis & Kuang Xu, 2017. "Flexible Queueing Architectures," Operations Research, INFORMS, vol. 65(5), pages 1398-1413, October.
    4. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    5. Legros, Benjamin & Jouini, Oualid & Dallery, Yves, 2015. "A flexible architecture for call centers with skill-based routing," International Journal of Production Economics, Elsevier, vol. 159(C), pages 192-207.
    6. Rodney B. Wallace & Ward Whitt, 2005. "A Staffing Algorithm for Call Centers with Skill-Based Routing," Manufacturing & Service Operations Management, INFORMS, vol. 7(4), pages 276-294, August.
    7. Tolga Tezcan & J. G. Dai, 2010. "Dynamic Control of N -Systems with Many Servers: Asymptotic Optimality of a Static Priority Policy in Heavy Traffic," Operations Research, INFORMS, vol. 58(1), pages 94-110, February.
    Full references (including those not matched with items on IDEAS)

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