Predicting Call Center Performance with Machine Learning

  • Siqiao Li
  • Qingchen Wang
  • Ger Koole
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)


In this paper we present a simulation-based machine learning framework to evaluate the performance of call centers having heterogeneous sets of servers and multiple types of demand. We first develop a simulation model for a call center with multi-skill agents and multi-class customers to sample quality of service (QoS) outcomes as measured by service level (SL). We then train a machine learning algorithm on a small number of simulation samples to quickly produce a look-up table of QoS for all candidate schedules. The machine learning algorithm is agnostic to the simulation and only uses information from the staff schedules. This allows our method to generalize across different real-life conditions and scenarios. Through two numerical examples using real-life call center scenarios we show that our method works surprisingly well, with out-of-sample fit (R-squared) of over 0.95 when comparing the machine learning prediction of SL to that of the ground truth from the simulation.


  1. 1.
    Atlason J, Epelman MA, Henderson SG. Optimizing call center staffing using simulation and analytic center cutting-plane methods. Manage Sci. 2008;54(2):295–309.CrossRefGoogle Scholar
  2. 2.
    Cezik MT, L’Ecuyer P. Staffing multiskill call centers via linear programming and simulation. Manage Sci. 2008;54(2):310–23.CrossRefGoogle Scholar
  3. 3.
    Avramidis AN, Chan W, Gendreau M, LEcuyer P, Pisacane O. Optimizing daily agent scheduling in a multiskill call center. Eur J Oper Res. 2010;200(3):822–32. ISSN 0377-2217.CrossRefGoogle Scholar
  4. 4.
    Bodur M, Luedtke JR. Mixed-integer rounding enhanced benders decomposition for multiclass servicesystem staffing and scheduling with arrival rate uncertainty. Manage Sci. 2017;63(7):2073–91.CrossRefGoogle Scholar
  5. 5.
    Ingolfsson A, Campello F, Wu X, Cabral E. Combining integer programming and the randomization method to schedule employees. Eur J Oper Res. 2010;202(1):153–63. ISSN 0377-2217.CrossRefGoogle Scholar
  6. 6.
    Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001;29(5):1189–232.CrossRefGoogle Scholar
  7. 7.
    Breiman L, Friedman J, Stone CJ, Olshen RA. Classification and regression trees. CRC press; 1984.Google Scholar
  8. 8.
    Chen T, Guestrin C. Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd, international conference on knowledge discovery and data mining. ACM;2016. p. 785–94.Google Scholar
  9. 9.
    Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY. Lightgbm: a highly efficient gradient boosting decision tree. Adv Neural Inf Proc Syst. 2017:3149–3157.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of MathematicsVrije Universiteit AmsterdamAmsterdamThe Netherlands
  2. 2.Amsterdam Business SchoolAmsterdamThe Netherlands

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