Can Customer Arrival Rates Be Modelled by Sine Waves?

Published Online:https://doi.org/10.1287/serv.2022.0045

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):665–688.CrossrefGoogle Scholar
  • Akşin Z, Ata B, Emadi S, Su C (2013) Structural estimation of callers’ delay sensitivity in call centers. Management Sci. 59(12):2727–2746.LinkGoogle Scholar
  • Alizadeh F, Eckstein J, Noyan N, Rudolf G (2008) Arrival rate approximation by nonnegative cubic splines. Oper. Res. 56(1):140–156.LinkGoogle Scholar
  • Armony M, Israelit S, Mandelbaum A, Marmor Y, Tseytlin Y, Yom-Tov G (2015) On patient flow in hospitals: A data-based queueing-science perspective. Stochastic Systems 5(1):146–194.LinkGoogle Scholar
  • Bassamboo A, Randhawa R, Zeevi A (2010) Capacity sizing under parameter uncertainty: Safety staffing principles revisited. Management Sci. 56(10):1668–1686.LinkGoogle Scholar
  • Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Ann. Statist. 29(4):1165–1188.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
  • Chan C, Dong J, Green L (2016) Queues with time-varying arrivals and inspections with applications to hospital discharge policies. Oper. Res. 65(2):469–495.LinkGoogle Scholar
  • Chen N, Lee D, Negahban S (2019) Super-resolution estimation of cyclic arrival rates. Ann. Statist. 47(3):1754–1775.CrossrefGoogle Scholar
  • Daw A, Pender J (2018) Queues driven by Hawkes processes. Stochastic Systems 8(3):192–229.LinkGoogle Scholar
  • Daw A, Pender J (2022) An ephemerally self-exciting point process. Adv. Appl. Probab. 54(2):340–403.CrossrefGoogle Scholar
  • Daw A, Castellanos A, Yom-Tov G, Pender J, Gruendlinger L (2021) The co-production of service: Modeling service times in contact centers using Hawkes processes. Working paper, Marshall School of Business, the University of Southern California, Los Angeles.Google Scholar
  • Dietz D (2011) Practical scheduling for call center operations. Omega 39(5):550–557.CrossrefGoogle Scholar
  • Eick S, Massey W, Whitt W (1993) Mt/G/∞ queues with sinusoidal arrival rates. Management Sci. 39(2):241–252.LinkGoogle Scholar
  • Feldman Z, Mandelbaum A, Massey W, Whitt W (2008) Staffing of time-varying queues to achieve time-stable performance. Management Sci. 54(2):324–338.LinkGoogle Scholar
  • Gao X, Zhu L (2018) Functional central limit theorems for stationary Hawkes processes and application to infinite-server queues. Queueing Systems 90(1):161–206.CrossrefGoogle Scholar
  • Green L, Kolesar P (1991) The pointwise stationary approximation for queues with nonstationary arrivals. Management Sci. 37(1):84–97.LinkGoogle Scholar
  • Green L, Kolesar P, Soares J (2001) Improving the SIPP approach for staffing service systems that have cyclic demands. Oper. Res. 49(4):549–564.LinkGoogle Scholar
  • Hillmer S, Hillmer B, McRoberts G (2004) The real costs of turnover: Lessons from a call center. Human Resource Planning 27(3):34–42.Google Scholar
  • Holm S (1979) A simple sequentially rejective multiple test procedure. Scandinavian J. Statit. 6(2):65–70.Google Scholar
  • Ibrahim R (2018) Managing queueing systems where capacity is random and customers are impatient. Production Oper. Management 27(2):234–250.CrossrefGoogle Scholar
  • Ibrahim R, Ye H, L’Ecuyer P, Shen H (2016) Modeling and forecasting call center arrivals: A literature survey and a case study. Internat. J. Forecasting 32(3):865–874.CrossrefGoogle Scholar
  • Jennings O, Mandelbaum A, Massey W, Whitt W (1996) Server staffing to meet time-varying demand. Management Sci. 42(10):1383–1394.LinkGoogle Scholar
  • Jongbloed G, Koole G (2001) Managing uncertainty in call centres using Poisson mixtures. Appl. Stochastic Models Bus. Indust. 17(4):307–318.CrossrefGoogle Scholar
  • Kim S, Whitt W (2014a) Are call center and hospital arrivals well modeled by nonhomogeneous Poisson processes? Manufacturing Service Oper. Management 16(3):464–480.LinkGoogle Scholar
  • Kim S, Whitt W (2014b) Choosing arrival process models for service systems: Tests of a nonhomogeneous Poisson process. Naval Res. Logist. 61(1):66–90.CrossrefGoogle Scholar
  • Koçağa Y, Armony M, Ward A (2015) Staffing call centers with uncertain arrival rates and co-sourcing. Production Oper. Management 24(7):1101–1117.CrossrefGoogle Scholar
  • Liu Y (2018) Staffing to stabilize the tail probability of delay in service systems with time-varying demand. Oper. Res. 66(2):514–534.LinkGoogle Scholar
  • Liu Y, Whitt W (2012) Stabilizing customer abandonment in many-server queues with time-varying arrivals. Oper. Res. 60(6):1551–1564.LinkGoogle Scholar
  • Mandelbaum A (2017) Service Enterprise Engineering (SEE) laboratory. Accessed November 28, https://seelab.net.technion.ac.il/.Google Scholar
  • Mandelbaum A, Zeltyn S (2009) The M/M/n+G queue: Summary of performance measures. Technical Note, Technion, Haifa, Israel.Google Scholar
  • Massey W, Parker G, Whitt W (1996) Estimating the parameters of a nonhomogeneous Poisson process with linear rate. Telecomm. Systems 5(2):361–388.CrossrefGoogle Scholar
  • Parker B (2011) The tide predictions for D-Day. Physics Today 64(9):35–40.CrossrefGoogle Scholar
  • Rice J, Rosenblatt M (1988) On frequency estimation. Biometrika 75(3):477–484.CrossrefGoogle Scholar
  • Saghafian S, Hopp W, van Oyen M, Desmond J, Kronick S (2012) Patient streaming as a mechanism for improving responsiveness in emergency departments. Oper. Res. 60(5):1080–1097.LinkGoogle Scholar
  • Shao N, Lii K (2011) Modelling non-homogeneous Poisson processes with almost periodic intensity functions. J. Royal Statist. Soc. B 73(1):99–122.CrossrefGoogle Scholar
  • Shi P, Chou M, Dai J, Ding D, Sim J (2015) Models and insights for hospital inpatient operations: Time-dependent ED boarding time. Management Sci. 62(1):1–28.Google Scholar
  • Steckley S, Henderson S, Mehrotra V (2005) Performance measures for service systems with a random arrival rate. Kuhl ME, Steiger NM, Brad Armstrong F, Joines JA, eds. Proc. Winter Simulation Conf. (Institute of Electrical and Electronics Engineers, Piscataway, NJ), 10.Google Scholar
  • Steckley S, Henderson S, Mehrotra V (2009) Forecast errors in service systems. Probab. Engrg. Inform. Sci. 23(2):305–332.CrossrefGoogle Scholar
  • Sun X, Liu Y (2021) Staffing many-server queues with autoregressive inputs. Naval Res. Logist. 68(3):312–326.CrossrefGoogle Scholar
  • Whitt W (2014) Heavy-traffic limits for queues with periodic arrival processes. Oper. Res. Lett. 42(6):458–461.CrossrefGoogle Scholar
  • Whitt W (2016) Heavy-traffic fluid limits for periodic infinite-server queues. Queueing Systems 84(1–2):111–143.CrossrefGoogle Scholar
  • Whitt W (2018) Time-varying queues. Working paper, Columbia University, New York.Google Scholar
  • Whitt W, Zhang X (2019) Forecasting arrivals and occupancy levels in an emergency department. Oper. Res. Health Care 21:1–18.CrossrefGoogle Scholar
  • Zeltyn S, Marmor Y, Mandelbaum A, Carmeli B, Greenshpan O, Mesika Y, Wasserkrug S, et al. (2011) Simulation-based models of emergency departments: Operational, tactical, and strategic staffing. ACM Trans. Model. Comput. Simulation 21(4):24.CrossrefGoogle Scholar
  • Zheng Z, Glynn P (2017) Fitting continuous piecewise linear Poisson intensities via maximum likelihood and least squares. Highland HJ, ed. Proc. 2017 Winter Simulation Conf. (Institute of Electrical and Electronics Engineers, Piscataway, NJ), 1740–1749.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.