Dynamic Call Center Routing Policies Using Call Waiting and Agent Idle Times

Published Online:https://doi.org/10.1287/msom.2014.0493

References

  • Akşin OZ, Armony M, Mehrotra V (2007) The modern call center: A multi-disciplinary perspective on operations management research. Production Oper. Management 16(6):665–688.CrossrefGoogle Scholar
  • Armony M, Ward AR (2010) Fair dynamic routing in large-scale heterogeneous-server systems. Oper. Res. 58(3):624–637.LinkGoogle Scholar
  • Armony M, Ward AR (2013) Blind fair routing in large-scale service systems with heterogeneous customers and servers. Oper. Res. 61(1):228–243.LinkGoogle Scholar
  • Atar R (2005) Scheduling control for queueing systems with many servers: Asymptotic optimality in heavy traffic. Ann. Appl. Probab. 15(4):2606–2650.CrossrefGoogle Scholar
  • Atar R, Giat C, Shimkin N (2010) The cμ/θ rule for many-server queues with abandonment. Oper. Res. 58(5):1427–1439.LinkGoogle Scholar
  • Atlason J, Epelman MA, Henderson SG (2004) Call center staffing with simulation and cutting plane methods. Ann. Oper. Res. 127(1–4):333–358.CrossrefGoogle Scholar
  • Avramidis AN, Chan W, Gendreau M, L'Ecuyer P, Pisacane O (2010) Optimizing daily agent scheduling in a multiskill call centers. Eur. J. Oper. Res. 200(3):822–832.CrossrefGoogle Scholar
  • Bassamboo A, Harrison JM, Zeevi A (2006) Design and control of a large call center: Asymptotic analysis of an LP-based method. Oper. Res. 54(3):419–435.LinkGoogle Scholar
  • Bell SL, Williams RJ (2001) Dynamic scheduling of a system with two parallel servers in heavy traffic with resource pooling: Asymptotic optimality of a threshold policy. Ann. Appl. Probab. 11(3):608–649.CrossrefGoogle Scholar
  • Bell SL, Williams RJ (2005) Dynamic scheduling of a parallel server system in heavy traffic with complete resource pooling: Asymptotic optimality of a threshold policy. Electronic J. Probab. 10(33):1044–1115.CrossrefGoogle Scholar
  • Botev ZI, Kroese DP, Rubinstein RY, L'Ecuyer P (2013) The cross-entropy method for optimization. Handbook of Statistics, Volume 31: Machine Learning (North-Holland, Amsterdam).CrossrefGoogle Scholar
  • Brown L, Gans N, Mandelbaum A, Sakov A, Shen H, Zeltyn S, Zhao L (2005) Statistical analysis of a telephone call center: A queueing-science perspective. J. Amer. Statist. Assoc. 100(469):36–50.CrossrefGoogle Scholar
  • Buist E, L'Ecuyer P (2005) A Java library for simulating contact centers. Kuhl ME, Steiger NM, Armstrong FB, Joines JA, eds. Proc. 2005 Winter Simulation Conf. (IEEE Press, Los Alamitos, CA), 556–565.CrossrefGoogle Scholar
  • Çezik MT, L'Ecuyer P (2008) Staffing multiskill call centers via linear programming and simulation. Management Sci. 54(2):310–323.LinkGoogle Scholar
  • de Boer P-T, Kroese DP, Mannor S, Rubinstein RY (2005) A tutorial on the cross-entropy method. Ann. Oper. Res. 134(1):19–67.CrossrefGoogle Scholar
  • Gans N, Zhou Y-P (2003) A call-routing problem with service-level constraints. Oper. Res. 51(2):255–271.LinkGoogle Scholar
  • Gans N, Koole G, Mandelbaum A (2003) Telephone call centers: Tutorial, review, and research prospects. Manufacturing Service Oper. Management 5(2):79–141.LinkGoogle Scholar
  • Gans N, Liu N, Mandelbaum A, Shen H, Ye H (2010) Service times in call centers: Agent heterogeneity and learning with some operational consequences. Berger J, Cai T, Johnstone I, eds. Borrowing Strength: Theory Powering Applications—A Festschrift for Lawrence D. Brown, Vol. 6 (Institute of Mathematical Statistics, Beachwood, OH), 99–123.CrossrefGoogle Scholar
  • Goldberg DE (1989) Genetic Algorithms in Search, Optimization and Machine Learning, 1st ed. (Addison-Wesley Longman, Boston).Google Scholar
  • Gurvich I, Whitt W (2009) Queue-and-idleness-ratio controls in many-server service systems. Math. Oper. Res. 34(2):363–396.LinkGoogle Scholar
  • Gurvich I, Whitt W (2010) Service-level differentiation in many-server service systems via queue-ratio routing. Oper. Res. 58(2):316–328.LinkGoogle Scholar
  • Gurvich I, Luedtke J, Tezcan T (2010) Staffing call centers with uncertain demand forecasts: A chance-constrained optimization approach. Management Sci. 56(7):1093–1115.LinkGoogle Scholar
  • Harrison JM, Zeevi A (2004) Dynamic scheduling of a multiclass queue in the Halfin-Whitt heavy traffic regime. Oper. Res. 52(2):243–257.LinkGoogle Scholar
  • Jouini O, Koole G, Roubos A (2013) Performance indicators for call centers with impatient customers. IIE Trans. 45(3):341–354.CrossrefGoogle Scholar
  • Koole G (2013) Call Center Optimization (MG Books, Amsterdam).Google Scholar
  • Koole G, Pot A (2005) Approximate dynamic programming in multi-skill call centers. Kuhl ME, Steiger NM, Armstrong FB, Joines JA, eds. Proc. 2005 Winter Simulation Conf. (IEEE Press, Los Alamitos, CA), 576–583.CrossrefGoogle Scholar
  • Koole G, Nielsen BF, Nielsen TB (2009) Optimization of overflow policies in call centers. Working paper, VU University Amsterdam, Amsterdam.Google Scholar
  • Larrañaga P, Etxeberria R, Lozano JA, Sierra B, Inza I, Peña JM (1999) A review of the cooperation between evolutionary computation and probabilistic graphical models. Ochoa A, Soto MR, Santana R, eds. Proc. Second Sympos. Artificial Intelligence (CIMAF-99), Habana, Cuba, 314–324.Google Scholar
  • Liao S, Van Delft C, Koole G, Jouini O (2010) Shift-scheduling of call centers with uncertain arrival parameters. MOSIM 2010, Hammamet, Tunisia, 1–10. http://www.enim.fr/mosim2010/program.php.Google Scholar
  • Mandelbaum A, Stolyar AL (2004) Scheduling flexible servers with convex delay costs: Heavy-traffic optimality of the generalized cμ-rule. Oper. Res. 52(6):836–855.LinkGoogle Scholar
  • Milner JM, Olsen TL (2008) Service-level agreements in call centers: Perils and prescriptions. Management Sci. 54(2):238–252.LinkGoogle Scholar
  • Mühlenbein H, Paaß G (1996) From recombination of genes to the estimation of distributions 1. Binary parameters. Voigt H-M, Ebeling W, Rechenberg I, Schwefel H-P, eds. Parallel Problem Solving from Nature—PPSN IV, Lecture Notes in Computer Science, Vol. 1141 (Springer, Berlin), 178–187.CrossrefGoogle Scholar
  • Perry O, Whitt W (2009) Responding to unexpected overloads in large-scale service systems. Management Sci. 55(8):1353–1367.LinkGoogle Scholar
  • Rubinstein RY, Kroese DP (2004) The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning (Springer-Verlag, New York).CrossrefGoogle Scholar
  • Sisselman ME, Whitt W (2007) Value-based routing and preference-based routing in customer contact centers. Production Oper. Management 16(3):277–291.CrossrefGoogle Scholar
  • Tezcan T, Dai JG (2010) Dynamic control of N-systems with many servers: Asymptotic optimality of a static priority policy in heavy traffic. Oper. Res. 58(1):94–110.LinkGoogle Scholar
  • van Mieghem JA (1995) Dynamic scheduling with convex delay costs: The generalized cμ rule. Ann. Appl. Probab. 5(3):809–833.CrossrefGoogle Scholar
  • van Mieghem JA (2003) Due-date scheduling: Asymptotic optimality of generalized longest queue and generalized largest delay rules. Oper. Res. 51(1):113–122.LinkGoogle Scholar
  • Wallace RB, Whitt W (2005) A staffing algorithm for call centers with skill-based routing. Manufacturing Service Oper. Management 7(4):276–294.LinkGoogle Scholar
  • Xu SH, Righter R, Shanthikumar JG (1992) Optimal dynamic assignment of customers to heterogeneous servers in parallel. Oper. Res. 40(6):1126–1138.LinkGoogle Scholar
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