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APPROXIMATE DYNAMIC PROGRAMMING TECHNIQUES FOR SKILL-BASED ROUTING IN CALL CENTERS*

Published online by Cambridge University Press:  30 July 2012

D. Roubos  and
S. Bhulai
D. Roubos
Affiliation:
VU University Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands E-mails: d.roubos@vu.nl; s.bhulai@vu.nl
S. Bhulai
Affiliation:
VU University Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, The Netherlands E-mails: d.roubos@vu.nl; s.bhulai@vu.nl
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Abstract

We consider the problem of dynamic multi-skill routing in call centers. Calls from different customer classes are offered to the call center according to a Poisson process. The agents are grouped into pools according to their heterogeneous skill sets that determine the calls that they can handle. Each pool of agents serves calls with independent exponentially distributed service times. Arriving calls that cannot be served directly are placed in a buffer that is dedicated to the customer class. We obtain nearly optimal dynamic routing policies that are scalable with the problem instance and can be computed online. The algorithm is based on approximate dynamic programming techniques. In particular, we perform one-step policy improvement using a polynomial approximation to relative value functions. We compare the performance of this method with decomposition techniques. Numerical experiments demonstrate that our method outperforms leading routing policies and has close to optimal performance.

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 26 , Issue 4 , October 2012 , pp. 581 - 591
Copyright
Copyright © Cambridge University Press 2012

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Footnotes

*

This study is an addendum to [3].

References

1.Asmussen, S., Nerman, O., & Olsson, M. (1996). Fitting phase type distributions via the em algorithm. Scandinavian Journal of Statistics 23(4): 419–441.Google Scholar
2.Bhulai, S. (2002). Markov decision processes: The control of high-dimensional systems. The Netherlands: Universal Press.Google Scholar
3.Bhulai, S. (2009). Dynamic routing policies for multi-skill call centers. Probability in the Engineering and Informational Sciences 23(1): 75–99.CrossRefGoogle Scholar
4.Bhulai, S. & Koole, G.M. (2003). A queueing model for call blending in call centers. IEEE Transactions on Automatic Control 48: 1434–1438.CrossRefGoogle Scholar
5.Franx, G.J., Koole, G., & Pot, S.A. (2006). Approximating multi-skill blocking systems by hyperexponential decomposition. Performance Evaluation 63: 799–824.CrossRefGoogle Scholar
6.Gans, N., Koole, G., & Mandelbaum, A. (2003). Telephone call centers: tutorial, review, and research prospects. Manufacturing Science and Operations Management 5: 79–141.Google Scholar
7.Gans, N. & Zhou, Y. (2003). A call-routing problem with service-level constraints. Operations Research 51: 255–271.CrossRefGoogle Scholar
8.Gurvich, I., Armony, M., & Mandelbaum, A. (2008). Service level differentation in call centers with fully flexible servers. Mangement Science 54: 279–294.CrossRefGoogle Scholar
9.Koole, G. & Pot, A. (2006). An overview of routing and staffing algorithms in multi-skill cusomter contact centers. Technical report, VU University Amsterdam, March.Google Scholar
10.Koole, G., Pot, A., & Talim, J. (2003). Routing heuristics for multi-skill call centers. In Ferrin, D., Chick, S., Sanchez, P.J., & Morrice, D.J. (eds.), Proceedings of the 2003 Winter Simulation Conference. New Orleans, Lousiana.CrossRefGoogle Scholar
11.Koole, G.M. & Mandelbaum, A. (2002). Queueing models of call centers: An introduction. Annals of Operations Research 113: 41–59.CrossRefGoogle Scholar
12.Perry, M. & Nilsson, A. (1992). Performance modeling of automatic call distributors: Assignable grade of service staffing. In XIV International Switching Symposium. Yokohama, Japan, pp. 294–298.Google Scholar
13.Puterman, M.L. (1994). Markov decision processes: Discrete stochastic dynamic programming. New York: John Wiley & Sons, Inc.CrossRefGoogle Scholar
14.Riordan, J. (1961). Stochastic service systems. New York: Wiley.Google Scholar
15.Shumsky, R.A. (2004). Approximation and analysis of a queueing system with flexible and specialized servers. Operations Research Letters 26.Google Scholar
16.Stanford, D.A. & Grassmann, W.K. (2000). Bilingual server call centres. In McDonald, D.R. & Turner, S.R.E. (eds.), Call centres, traffic and performance, vol. 28, pp. 31–48. Providence, RI: Fields Institute Communications.Google Scholar
17.Wallace, R. & Whitt, W. (2005). A staffing algorithm for call centers with skill-based routing. Manufacturing Science and Operations Management 7(4): 276–294.Google Scholar