Hostname: page-component-7b7567c8c-7pwfn Total loading time: 0 Render date: 2026-11-14T19:53:08.979Z Has data issue: false hasContentIssue false
  • English
  • Français

DYNAMIC ROUTING POLICIES FOR MULTISKILL CALL CENTERS

Published online by Cambridge University Press:  13 November 2008

Sandjai Bhulai
Sandjai Bhulai
Affiliation:
VU University Amsterdam, Faculty of Sciences, 1081 HV Amsterdam, The Netherlands E-mail: sbhulai@few.vu.nl
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

We consider the problem of routing calls dynamically in a multiskill call center. Calls from different skill classes are offered to the call center according to a Poisson process. The agents in the center are grouped according to their heterogeneous skill sets that determine the classes of calls they can serve. Each agent group serves calls with independent exponentially distributed service times. We consider two scenarios. The first scenario deals with a call center with no buffers in the system, so that every arriving call either has to be routed immediately or has to be blocked and is lost. The objective in the system is to minimize the average number of blocked calls. The second scenario deals with call centers consisting of only agents that have one skill and fully cross-trained agents, where calls are pooled in common queues. The objective in this system is to minimize the average number of calls in the system. We obtain nearly optimal dynamic routing policies that are scalable with the problem instance and can be computed online. The algorithm is based on one-step policy improvement using the relative value functions of simpler queuing systems. Numerical experiments demonstrate the good performance of the routing policies. Finally, we discuss how the algorithm can be used to handle more general cases with the techniques described in this article.

Information

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 23 , Issue 1 , January 2009, pp. 101 - 119
Copyright
Copyright © Cambridge University Press 2009

References

1.Bertsekas, D. & Tsitsiklis, J. (1996). Neuro-dynamic programming. Bellmont, MA: Athena Scientific.Google Scholar
2.Bhulai, S. & Koole, G. (2003). On the structure of value functions for threshold policies in queuing models. Journal of Applied Probability 40: 613–622.CrossRefGoogle Scholar
3.Bhulai, S. & Koole, G. (2003). A queueing model for call blending in call centers. IEEE Transactions on Automatic Control 48: 1434–1438.CrossRefGoogle Scholar
4.Borst, S. & Seri, P. (2000). Robust algorithms for sharing agents with multiple skills. Working paper, Bell Laboratories, Murray Hill, NJ.Google Scholar
5.Chevalier, P., Tabordon, N. & Shumsky, R. (2004). Routing and staffing in large call centers with specialized and fully flexible servers. Louvain-la-Neuve, Belgium: Université Catholique de Louvain.Google Scholar
6.Franx, G., Koole, G. & Pot, S. (2006). Approximating multi-skill blocking systems by hyperexponential decomposition. Performance Evaluation 63: 799–824.CrossRefGoogle Scholar
7.Gans, N., Koole, G. & Mandelbaum, A. (2003). Telephone call centers: tutorial, review, and research prospects. Manufacturing and Service Operations Management 45: 79–141.CrossRefGoogle Scholar
8.Gans, N. & Zhou, Y. (2003). A call-routing problem with service-level constraints. Operations Research 51: 255–271.CrossRefGoogle Scholar
9.Gurvich, I., Armony, M. & Mandelbaum, A. (2008). Service level differentation in call centers with fully flexible servers. Management Science 54: 279–294.CrossRefGoogle Scholar
10.Koole, G. (1995). Stochastic scheduling and dynamic programming. CWI Tract 113. Amsterdam: CWI.Google Scholar
11.Koole, G. (2003). Redefining the service level in call centers. Technical report, Vrije Universiteit, Amsterdam.Google Scholar
12.Koole, G. & Mandelbaum, A. (2002). Queueing models of call centers: an introduction. Annals of Operations Research 113: 41–59.CrossRefGoogle Scholar
13.Koole, G. & Nain, P. (2000). On the value function of a priority queue with an application to a controlled polling model. Queueing Systems 34: 199–214.CrossRefGoogle Scholar
14.Koole, G. & Talim, J. (2000). Exponential approximation of multi-skill call centers architecture. In Proceedings of QNETs 2000, pp. 23/1–23/10.Google Scholar
15.Mickens, R. (1990). Difference equations: Theory and applications. New York: Chapman & Hall.Google Scholar
16.Örmeci, E. (2004). Dynamic admission control in a call center with one shared and two dedicated service facilities. IEEE Transactions on Automatic Control 49: 1157–1161.CrossRefGoogle Scholar
17.Ott, T. & Krishnan, K. (1992). Separable routing: A scheme for state-dependent routing of circuit switched telephone traffic. Annals of Operations Research 35: 43–68.CrossRefGoogle Scholar
18.Perry, M. & Nilsson, A. (1992). Performance modeling of automatic call distributors: Assignable grade of service staffing. In XIV International Switching Symposium, pp. 294–298.Google Scholar
19.Puterman, M. (1994). Markov decision processes: Discrete stochastic dynamic programming. New York: Wiley.CrossRefGoogle Scholar
20.Sassen, S., Tijms, H. & Nobel, R. (1997). A heuristic rule for routing customers to parallel servers. Statistica Neerlandica 51: 107–121.CrossRefGoogle Scholar
21.Shumsky, R. (2004). Approximation and analysis of a queueing system with flexible and specialized servers. OR Spektrum 26: 307–330.CrossRefGoogle Scholar
22.Stanford, D. & Grassmann, W. (2000). Bilingual server call centres. In McDonald, D. & Turner, S. (eds.) Call centres, traffic and performance, Providence, RI: American Mathematical Society. Fields Institute Communications, Vol. 28, pp. 31–48.Google Scholar
23.Tabordon, N. (2002). Modeling and optimizing the management of operator training in a call center. Ph.D thesis, Université Catholique de Louvain.Google Scholar
24.Wallace, R. & Whitt, W. (2005). A staffing algorithm for call centers with skill-based routing. Manufacturing and Service Operations Management pp. 276–294.CrossRefGoogle Scholar