On Customer Contact Centers with a Call-Back Option: Customer Decisions, Routing Rules, and System Design

Published Online:https://doi.org/10.1287/opre.1030.0088

References

  • Anderson S. P., de Palma A., Thisee J. F.Discrete Choice Theory of Product Differentiation (1996) (MIT Press, Cambridge, MA) Google Scholar
  • Armony M. C., Maglaras C. Contact centers with a call-back option and real-time delay information. Oper. Res (2004) . ForthcomingGoogle Scholar
  • Bell S. L., Williams R. J. Dynamic scheduling of a system with two parallel servers in heavy traffic with resource pooling: Asymptotic optimality of a threshold policy. Ann. Appl. Probab (2001) 11:608–649CrossrefGoogle Scholar
  • Billingsley P.Convergence of Probability Measures (1968) (John Wiley and Sons, New York) Google Scholar
  • Borst M. Dimensioning large call centers. Oper. Res (2004) 52(1):17–34LinkGoogle Scholar
  • Bramson M. State space collapse with applications to heavy-traffic limits for multiclass queueing networks. Queueing Systems (1998) 30:89–148CrossrefGoogle Scholar
  • Brandt A., Brandt M. On a two-queue priority system with impatience and its applications to a call center. Methodology Comput. Appl. Probab (1999) 1:191–210CrossrefGoogle Scholar
  • Call center statistics (2001) . www.callcenternews.com/resources/statistics.shtmlGoogle Scholar
  • Dai J. G. On positive Harris recurrence of multiclass queueing networks: A unified approach via fluid limit models. Ann. Appl. Probab (1995) 5:49–77CrossrefGoogle Scholar
  • Dai J. G., Williams R. J. Existence and uniqueness of semimartingale reflecting Brownian motions in convex polyhedrons. Theory Probab. Its Appl (1995) 50:3–53Google Scholar
  • Fleming P. A., Stolyar B., Simon B. Heavy traffic limit for a mobile phone system loss model. Proc. 2nd Internat. Conf. Telecomm. Syst. Mod. Anal (1994) Nashville, TNGoogle Scholar
  • Gans N., Zhou Y.-P. A call-routing problem with service-level constraints. Oper. Res (2003) 51(2):255–271LinkGoogle Scholar
  • Garnett O., Mandelbaum A., Reiman M. Designing a call center with impatient customers. Manufacturing Service Oper. Management (2002) 4(3):208–227LinkGoogle Scholar
  • Glynn P. W., Heyman D., Sobel M. Diffusion approximations. Stochastic Models. Handbooks in OR & MS (1990) 2(North-Holland, Amsterdam, The Netherlands) 145–198CrossrefGoogle Scholar
  • Green L. V., Kolesar P. J. The pointwise stationary approximation for queues with nonstationary arrivals. Management Sci (1991) 37(1):84–97LinkGoogle Scholar
  • Halfin S., Whitt W. Heavy-traffic limits for queues with many exponential servers. Oper. Res (1981) 29(3):567–588LinkGoogle Scholar
  • Harrison J. M., Kelly F., Zachary S., Ziedins I. The BIGSTEP approach to flow management in stochastic processing networks. Stochastic Networks: Theory and Applications (1996) (Oxford University Press, Oxford, U.K.) 57–90CrossrefGoogle Scholar
  • Hassin R., Haviv M. Equilibrium strategies for queues with impatient customers. Oper. Res. Lett (1995) 17:41–45CrossrefGoogle Scholar
  • Jennings O., Mandelbaum A., Massey W., Whitt W. Server staffing to meet time-varying demand. Management Sci (1996) 42(10):1383–1394LinkGoogle Scholar
  • Kelly F. P., Laws C. N. Dynamic routing in open queueing models: Brownian models, cut constraints and resource pooling. Queueing Systems (1993) 13:47–86CrossrefGoogle Scholar
  • Kolesar P. J., Green L. V. Insights on service system design from a normal approximation to Erlang's delay formula. Prod. Oper. Management (1998) 7:282–293CrossrefGoogle Scholar
  • Maglaras C. Discrete-review policies for scheduling stochastic networks: Trajectory tracking and fluid-scale asymptotic optimality. Ann. Appl. Probab (2000) 10(3):897–929CrossrefGoogle Scholar
  • Maglaras C., Van Mieghem J. Admission and sequencing control under delay constraints with applications to GPS and GLQ. Eur. J. Oper. Res (2004) . ForthcomingGoogle Scholar
  • Maglaras C., Zeevi A. Pricing and capacity sizing for systems with shared resources: Approximate solutions and scaling relations. Management Sci (2003) 49(8):1018–1038LinkGoogle Scholar
  • Maglaras C., Zeevi A. Diffusion approximations for a Markovian service system with "guaranteed" and "best-effort" service levels. Math. Oper. Res (2004) . ForthcomingLinkGoogle Scholar
  • Mandelbaum A., Shimkin N. A model for rational abandonments from invisible queues. Queueing Systems (2000) 36:141–173CrossrefGoogle Scholar
  • Plambeck E., Kumar S., Harrison J. M. Leadtime constraints in stochastic processing networks under heavy traffic conditions. Queueing Systems (2001) 39:23–54CrossrefGoogle Scholar
  • Puhalskii A. On the invariance principle for the first passage time. Math. Oper. Res (1994) 19(2):946–954LinkGoogle Scholar
  • Puhalskii A., Reiman M. The multiclass GI/PH/N queue in the Halfin-Whitt regime. Adv. Appl. Probab (2000) 32(2):564–595CrossrefGoogle Scholar
  • Reiman M. I. Open queueing networks in heavy traffic. Math. Oper. Res (1984) 9:441–458LinkGoogle Scholar
  • Saltzman R. M., Mehrotra V. A call center uses simulation to drive strategic change. Interfaces (2001) 31(3):87–101LinkGoogle Scholar
  • Talluri K., van Ryzin G. Revenue management under a general discrete-choice model of demand. Management Sci (2004) . ForthcomingLinkGoogle Scholar
  • Teh Y.-C., Ward A. Critical thresholds for dynamic routing in queueing networks. Queueing Systems (2002) 42:297–316CrossrefGoogle Scholar
  • Whitt W., Clarke A. B. Heavy traffic limits for queues: A survey. Mathematical Methods in Queueing Theory. Lecture Notes in Econom. and Math. Systems98(Springer-Verlag, New York) 307–350Google Scholar
  • Whitt W. Using different response-time requirements to smooth time-varying demand for service. Oper. Res. Lett (1999) 24:1–10CrossrefGoogle Scholar
  • Whitt W. How multiserver queues scale with growing congestion-dependent demand. Oper. Res (2003) 51(4):531–542LinkGoogle Scholar
  • Zohar E., Mandelbaum A., Shimkin N. Adaptive behavior of impatient customers in telequeues: Theory and empirical support. Management Sci (2002) 48:566–583LinkGoogle Scholar
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