Parametric Forecasting and Stochastic Programming Models for Call-Center Workforce Scheduling

References

• Akşin Z, Armony M, Mehrotra V (2007) The modern call-center: A multi-disciplinary perspective on operations management research. Production Oper. Management 16(6):655–688.Google Scholar
• Aldor-Noiman S, Feigin PD, Mandelbaum A (2009) Workload forcasting for a call center: Methodology and a case study. Ann. Appl. Statist. 3(4):1403–1447.Crossref, Google Scholar
• Armony M, Plambeck E, Seshadri S (2009) Sensitivity of optimal capacity to customer impatience in an unobservable M/M/S queue (why you shouldn’t shout at the DMV). Manufacturing Service Oper. Management 11(1):19–32.Link, Google Scholar
• Avramidis AN, Deslauriers A, L’Ecuyer P (2004) Modeling daily arrivals to a telephone call center. Management Sci. 50:896–908.Link, Google Scholar
• Bassamboo A, Zeevi A (2009) On a data-driven method for staffing large call centers. Oper. Res. 57(3):714–726.Link, Google Scholar
• Bassamboo A, Harrison JM, Zeevi A (2005) Dynamic routing and admission control in high-volume service systems: Asymptotic analysis via multi-scale fluid limits. Queueing Systems 51(3–4):249–285.Crossref, Google 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.Link, Google Scholar
• Bertsimas D, Doan X (2010) Robust and data-driven approaches to call centers. Eur. J. Oper. Res. 207(2):1072–1085.Crossref, Google Scholar
• Birge JR, Louveaux F (1997) Introduction to Stochastic Programming (Springer, New York).Google Scholar
• Brown LD, Cai T, Zhang R, Zhao L, Zhou H (2010) The root–unroot algorithm for density estimation as implemented via wavelet block thresholding. Probab. Theory Related Fields 146(3–4):401–433.Crossref, Google 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.Crossref, Google Scholar
• Chen B, Henderson SG (2001) Two issues in setting call centre staffing levels. Ann. Oper. Res. 108(1–4):175–192.Crossref, Google Scholar
• DeGroot MH (1970) Optimal Statistical Decisions (McGraw-Hill, New York).Google Scholar
• Ding S, Koole G (2014) Optimal call center forecasting and staffing under arrival rate uncertainty. Working paper, VU University Amsterdam, Amsterdam.Google Scholar
• Douc R, Moulines E, Olsson J, van Handel R (2011) Consistency of the maximum likelihood estmator for general hidden Markov models. Ann. Statist. 39(1):474–513.Crossref, Google Scholar
• Gans N, Koole G, Mandelbaum A (2003) Telephone call centers: Tutorial, review, and research prospects. Manufacturing Service Oper. Management 5(2):79–141.Link, Google Scholar
• Gans N, Shen H, Ye H, Zhou Y-P (2015) Long-run average optimality of AR(1)-driven workforce scheduling models. In preparation.Google Scholar
• Garnett O, Mandelbaum A, Reiman M (2002) Designing a call center with impatient customers. Manufacturing Service Oper. Management 4(3):208–227.Link, Google Scholar
• Grassman WK (1988) Finding the right number of servers in real-world queuing systems. Interfaces 18(2):94–104.Link, Google Scholar
• Green LV, Kolesar PJ, Soares J (2001) Improving the SIPP approach for staffing service systems that have syclic demands. 49(4):549–564.Google Scholar
• Gurvich I, Luedtke J, Tezcan T (2010) Staffing call centers with uncertain demand forecasts: A chance-constrained approach. Management Sci. 56(7):1093–1115.Link, Google Scholar
• Harrison JM, Zeevi A (2005) A method for staffing large call centers based on stochastic fluid models. Manufacturing Service Oper. Management 7(1):20–36.Link, Google Scholar
• Ibrahim R, L’Ecuyer P (2013) Forecasting call center arrivals: Fixed-effects, mixed-effects, and bivariate models. Manufacturing Service Oper. Management 15(1):72–85.Link, Google Scholar
• Ibrahim R, L’Ecuyer P, Régnard N, Shen H (2012) On the modeling and forecasting of call center arrivals. Laroque C, Himmelspach J, Pasupathy R, Rose O, Uhrmacher AM, eds. Proc. 2012 Winter Simulation Conf. (IEEE, Piscataway, NJ),256–267.Crossref, Google Scholar
• Jongbloed G, Koole G (2001) Managing uncertainty in call centres using Poisson mixtures. Appl. Stochastic Models Bus. Indust. 17(4):307–318.Crossref, Google Scholar
• Jouini O, Pot A, Koole G, Dallery Y (2010) Online scheduling policies for multiclass call centers with impatient customers. Eur. J. Oper. Res. 207(1):258–268.Crossref, Google Scholar
• Kim S-H, Whitt W (2014) Are call center and hospital arrivals well modeled by nonhomogeneous Poisson processes? Manufacturing Service Oper. Management 16(3):464–480.Link, Google Scholar
• Liao S, Koole G, van Delft C, Jouini O (2012) Staffing a call center with uncertain non-stationary arrival rate and flexibility. OR Spectum 34(3):691–721.Crossref, Google Scholar
• Maman S (2009) Uncertainty in the demand for service: The case of call centers and emergency departments. Master’s thesis, Technion–Israel Institute of Technology, Haifa.Google Scholar
• Mandelbaum A, Zeltyn S (2009) The M/M/n + G queue: Summary of performance measures. Technical note, Technion–Israel Institute of Technology, Haifa.Google Scholar
• Mehrotra V, Ozluk O, Saltzman R (2010) Intelligent procedures for intra-day updating of call center agent schedules. Production Oper. Management 19(3):353–367.Crossref, Google Scholar
• Miller AC, Rice TR (1983) Discrete approximations of probability distributions. Management Sci. 29(3):352–362.Link, Google Scholar
• Oreshkin BN, Regnard N, L’Ecuyer P (2014) Rate-based daily arrival process models with application to call centers. Working paper, Université de Montréal, Montreal.Google Scholar
• Robbins TR, Harrison TP (2010) A stochastic programming model for scheduling call centers with global service level agreements. Eur. J. Oper. Res. 207:1608–1617.Crossref, Google Scholar
• Robbins TR, Medeiros DJ, Dum P (2006) Evaluating arrival rate uncertainty in call centers. Perrone LF, Wieland FP, Liu J, Lawson BG, Nicol DM, Fujimoto RM, eds. Proc. 2006 Winter Simulation Conf. (IEEE, Piscataway, NJ), 2180–2187.Crossref, Google Scholar
• Robbins TR, Medeiros DJ, Harrison TJ (2010) Cross training in call centers with uncertain arrivals and global service level agreements. Internat. J. Oper. Quant. Management 16(3):307–329.Google Scholar
• Ross AM (2001) Queueing systems with daily cycles and stochastic demand with uncertain parameters. Ph.D. dissertation, University of California, Berkeley, Berkeley.Google Scholar
• Ross SM (1996) Stochastic Processes, 2nd ed. (Wiley, New York).Google Scholar
• Shapiro A (2000) Stochastic programming by Monte Carlo simulation methods. Stochastic Programming e-Print Series, 2000(3), http://www.speps.org.Google Scholar
• Shapiro A, Philpott A (2007) A tutorial on stochastic programming. Technical note, Georgia Institute of Technology, Atlanta.Google Scholar
• Shen H, Huang JZ (2008) Interday forecasting and intraday updating of call center arrivals. Manufacturing Service Oper. Management 10(3):391–410.Link, Google Scholar
• Steckley SG, Henderson SG, Mehrotra V (2009) Forecast errors in service systems. Probab. Engrg. Informational Sci. 23(2):305–332.Crossref, Google Scholar
• Taylor JW (2012) Density forecasting of intraday call center arrivals using models based on exponential smoothing. Management Sci. 58(3):534–549.Link, Google Scholar
• Weinberg J, Brown LD, Stroud JR (2007) Bayesian forecasting of an inhomogeneous Poisson process with applications to call center data. J. Amer. Statist. Assoc. 102(480):1185–1199.Crossref, Google Scholar
• Whitt W (1999) Dynamic staffing in a telephone call center aiming to immediately answer all calls. 1999. Oper. Res. Lett. 24(5):205–212.Crossref, Google Scholar
• Zan J, Hasenbein J, Morton D (2013) Staffing large service systems under arrival-rate uncertainty. Working paper, University of Texas at Austin, Austin.Google Scholar
• Zohar E, Mandelbaum A, Shimkin N (2002) Adaptive behavior of impatient customers in tele-queues: Theory and empirical support. Management Sci. 48(4):566–583.Link, Google Scholar