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STAFFING TO STABILIZE BLOCKING IN LOSS MODELS WITH TIME-VARYING ARRIVAL RATES

Published online by Cambridge University Press:  09 December 2015

Andrew Li
Affiliation:
Operations Research Center, M.I.T. 77 Mass Ave, Bldg E40-130, Cambridge, MA 02139-4307, USA E-mail: [email protected]
Ward Whitt
Affiliation:
Department of Industrial Engineering and Operations Research, Columbia University, New York, NY 10027, USA E-mail: [email protected]
Jingtong Zhao
Affiliation:
Department of Industrial Engineering and Operations Research Columbia University, New York, NY 10027, USA E-mail: [email protected]

Abstract

The modified-offered-load approximation can be used to choose a staffing function (the time-varying number of servers) to stabilize delay probabilities at target levels in multi-server delay models with time-varying arrival rates, with or without customer abandonment. In contrast, as we confirm with simulations, it is not possible to stabilize blocking probabilities to the same extent in corresponding loss models, without extra waiting space, because these probabilities necessarily change dramatically after each staffing change. Nevertheless, blocking probabilities can be stabilized provided that we either randomize the times of staffing changes or average the blocking probabilities over a suitably small time interval. We develop systematic procedures and study how to choose the averaging parameters.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2015 

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References

1.Davis, J.L., Massey, W.A., & Whitt, W. (1995). Sensitivity to the service-time distribution in the nonstationary Erlang loss model. Management Science 41(6): 11071116.Google Scholar
2.Defraeye, M. & van Nieuwenhuyse, I. (2013). Controlling excessive waiting times in small service systems with time-varying demand: an extension of the ISA algorithm. Decision Support Systems 54(4): 15581567.CrossRefGoogle Scholar
3.Eick, S.G., Massey, W.A., & Whitt, W. (1993). M t/G/∞ queues with sinusoidal arrival rates. Management Science 39: 241252.Google Scholar
4.Feldman, Z., Mandelbaum, A., Massey, W.A., & Whitt, W. (2008). Staffing of time-varying queues to achieve time-stable performance. Management Science 54(2): 324338.Google Scholar
5.Green, L.V., Kolesar, P.J., & Whitt, W. (2007). Coping with time-varying demand when setting staffing requirements for a service system. Production Operations Management 16: 1329.Google Scholar
6.Grier, N., Massey, W.A., McKoy, T., & Whitt, W. (1997). The time-dependent Erlang loss model with retrials. Telecommunications Systems 7: 253265.Google Scholar
7.Hampshire, R.C. & Massey, W.A. (2010). Dynamic optimization with applications to dynamic rate queues. Tutorials in Operations Research 27: 208247.Google Scholar
8.Hampshire, R.C., Massey, W.A., & Wang, Q. (2009). Dynamic pricing to control loss systems. Probability in the Engineering and Informational Sciences 23: 357383.Google Scholar
9.He, B., Liu, Y., & Whitt, W. (2015). Staffing a service system with non-Poisson nonstationary arrivals. Columbia University, http://www.columbia.edu/~ww2040/allpapers.html.Google Scholar
10.Jagerman, D.L. (1975). Nonstationary blocking in telephone traffic. Bell System Technical Journal 54: 625661.Google Scholar
11.Jennings, O.B., Mandelbaum, A., Massey, W.A., & Whitt, W. (1996). Server staffing to meet time-varying demand. Management Science 42: 13831394.CrossRefGoogle Scholar
12.Kim, S.-H. & Whitt, W. (2013). Estimating waiting times with the time-varying Little's law. Probability in the Engineering and Informational Sciences 27: 471506.CrossRefGoogle Scholar
13.Li, A. & Whitt, W. (2014). Approximate blocking probabilities for loss models with independence and distribution assumptions relaxed. Performance Evaluation 80: 82101.CrossRefGoogle Scholar
14.Liu, Y. & Whitt, W. (2012). The G t/GI/s t+GI many-server fluid queue. Queueing Systems 71: 405444.CrossRefGoogle Scholar
15.Liu, Y. & Whitt, W. (2012). Stabilizing customer abandonment in many-server queues with time-varying arrivals. Operations Research 60: 15511564.Google Scholar
16.Liu, Y. & Whitt, W. (2014). Stabilizing performance in a service system with time-varying arrivals and customer feedback. Columbia University, http://www.columbia.edu/ww2040.Google Scholar
17.Liu, Y. & Whitt, W. (2014). Stabilizing performance in networks of queues with time-varying arrival rates. Probability in the Engineering and Informational Sciences 28: 419449.CrossRefGoogle Scholar
18.Massey, W.A. & Whitt, W. (1994). An analysis of the modified offered load approximation for the nonstationary Erlang loss model. Annul Applied Probability 4: 11451160.Google Scholar
19.Massey, W.A. & Whitt, W. (1994). A stochastic model to capture space and time dynamics in wireless communication systems. Probability in the Engineering and Informational Sciences 8: 541569.Google Scholar
20.Melamed, B. & Whitt, W. (1990). On arrivals that see time averages. Operations Research 38(1): 156172.Google Scholar
21.Pender, J. (2015). Nonstationary loss queues via cumulant moment approximations. Probability in the Engineering and Information Sciences 29: 2749.Google Scholar
22.Pender, J. & Massey, W.A. (2014). Approximating and stabilizing dynamic rate Jackson networks with abandonment. Cornell University, http://www.people.orie.cornell.edu/jpender/Google Scholar
23.Stolletz, R. (2008). Approximation of the nonstationary M(t)/M(t)/c(t) queue using stationary models: the stationary backlog-carryover approach. European Journal of Operations Research 190(2): 478493.Google Scholar
24.Whitt, W. (1982). Approximating a point process by a renewal process: two basic methods. Operations Research 30: 125147.CrossRefGoogle Scholar
25.Whitt, W. (1984). Heavy-traffic approximations for service systems with blocking. AT&T Bell Laboratories Technical Journal 63: 689708.Google Scholar
26.Wolfe, R.W. (1977). The effect of service time regularity on system performance. In Chandy, K.M., & Reiser, M., (eds.), Computer Performance. Amsterdam: North-Holland, pp. 297304.Google Scholar
27.Yom-Tov, G. & Mandelbaum, A. (2014). Erlang R: a time-varying queue with reentrant customers, in support of healthcare staffing. Manufacturing and Service Operations Management 16(2): 283299.CrossRefGoogle Scholar