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OPTIMIZATION OF OVERFLOW POLICIES IN CALL CENTERS

Published online by Cambridge University Press:  24 April 2015

G.M. Koole,
B.F. Nielsen and
T.B. Nielsen
G.M. Koole
Affiliation:
Department of Mathematics, VU University Amsterdam, De Boelelaan 1081, 1081 HV Amsterdam the Netherlands E-mail: ger.koole@vu.nl
B.F. Nielsen
Affiliation:
Department of Informatics and Mathematical Modelling, Technical University of Denmark, Richard Petersens Plads, 2800 Kgs. Lyngby Denmark E-mail: bfni@dtu.dk
T.B. Nielsen
Affiliation:
Department of Informatics and Mathematical Modelling, Technical University of Denmark, Richard Petersens Plads, 2800 Kgs. Lyngby Denmark E-mail: bfni@dtu.dk
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Abstract

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We examine how overflow policies in a multi-skill call center should be designed to accommodate performance measures that depend on waiting time percentiles such as service level. This is done using a discrete Markovian approximation of the waiting time of the first customers waiting in line. A Markov decision chain is used to determine the optimal policy. This policy outperforms considerably the ones used most often in practice, which use a fixed threshold. The present method can be used also for other call-center models and other situations where performance is based on actual waiting times and customers are treated in a FCFS order.

Information

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 29 , Issue 3 , July 2015, pp. 461 - 471
Copyright
Copyright © Cambridge University Press 2015 

References

1. Barth, W., Manitz, M. & Stolletz, R. (2010). Analysis of two-level support systems with time-dependent overflow – a banking application. Production and Operations Management 19(6): 757–768.Google Scholar
2. Bekker, R., Koole, G., Nielsen, B.F. & Nielsen, T.B. (2011). Queues with waiting time dependent service. Queueing Systems, 68(1): 61–78.Google Scholar
3. Gans, N., Koole, G. & Mandelbaum, A. (2003). Telephone call centers: Tutorial, review, and research prospects. Manufacturing and Service Operations Management 5(2): 79–141.Google Scholar
4. Groenevelt, R., Koole, G. & Nain, P. (2002). On the bias vector of a two-class preemptive priority queue. Mathematical Methods of Operations Research 55(1): 107–120.Google Scholar
5. Jackson, J.R. (1960). Some problems in queueing with dynamic priorities. Naval Research Logistics Quarterly 7: 235–249.Google Scholar
6. Jackson, J.R. (1961). Queues with dynamic priority discipline. Management Science 8(1): 18–34.Google Scholar
7. Kleinrock, L. & Finkelstein, R.P. (1967). Time dependent priority queues. Operations Research 15(1): 104–116.Google Scholar
8. Koole, G., Nielsen, B.F. & Nielsen, T.B. (2012). First in line waiting times as a tool for analyzing queueing systems. Operations Research 60(5): 1258–1266.Google Scholar
9. Perry, M. & Nilsson, A. (1992). Performance modelling of automatic call distributors: assignable grade of service staffing. International Switching Symposium 1992. “Diversification and Integration of Networks and Switching Technologies Towards the 21st Century” Proceedings, Vol. 2, pp. 294–298.Google Scholar
10. Puterman, M.L. (2005). Markov Decision processes. Discrete stochastic dynamic programming. 2nd ed. New York: Wiley.Google Scholar
11. Tijms, H.C. (2003). A first course in Stochastic models. 2nd ed. New York: Wiley.Google Scholar