Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-27T07:30:39.417Z Has data issue: false hasContentIssue false
  • English
  • Français

DYNAMIC PRICING TO CONTROL LOSS SYSTEMS WITH QUALITY OF SERVICE TARGETS

Published online by Cambridge University Press:  16 February 2009

Robert C. Hampshire ,
William A. Massey  and
Qiong Wang
Robert C. Hampshire
Affiliation:
Carnegie Mellon University, Pittsburgh, PA E-mail: [email protected]
William A. Massey
Affiliation:
Princeton University, Princeton, NJ E-mail: [email protected]
Qiong Wang
Affiliation:
Bell Laboratories, Murray Hill, NJ E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Numerous examples of real-time services arise in the service industry that can be modeled as loss systems. These include agent staffing for call centers, provisioning bandwidth for private line services, making rooms available for hotel reservations, and congestion pricing for parking spaces. Given that arriving customers make their decision to join the system based on the current service price, the manager can use price as a mechanism to control the utilization of the system. A major objective for the manager is then to find a pricing policy that maximizes total revenue while meeting the quality of service targets desired by the customers. For systems with growing demand and service capacity, we provide a dynamic pricing algorithm. A key feature of our solution is congestion pricing. We use demand forecasts to anticipate future service congestion and set the present price accordingly.

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 23 , Issue 2 , April 2009 , pp. 357 - 383
Copyright
Copyright © Cambridge University Press 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Bailey, J. & McKnight, L. (1997). Internet economics. Cambridge, MA: The MIT Press.Google Scholar
2.Courcoubetis, C.A., Dimakis, A. & Reiman, M.I. (2001). Providing band width guarantees over a best-effort network: call admission and pricing. Proceedings of IEEE INFOCOM 2001, pp. 459–467.Google Scholar
3.Dewan, S. & Mendelson, H. (1990). User delay cost and internal pricing for a service facility. Management Science 36(12): 1502–1517.CrossRefGoogle Scholar
4.Dixit, A.K. (1990). Optimization in economic theory. Oxford: Oxford University Press.CrossRefGoogle Scholar
5.Eick, S., Massey, W.A. & Whitt, W. (1993). The physics of the M(t)/G/∞ queue. Operations Research 41(4): 400–408.CrossRefGoogle Scholar
6.Erlang, A.K. (1918). Solutions of some problems in the theory of probabilities of significance in automatic telephone exchanges. The Post Office Electrical Engineers’ Journal 10: 189–197.Google Scholar
7.Ewing, G.M. (1985). Calculus of variations with applications. New York: Dover Publications.Google Scholar
8.Fan-Orzechowski, X. & Feinberg, E.A. (2007). Optimality of randomized trunk reservation for a problem with multiple constraints. Probability in the Engineering and Informational Sciences 21(2): 189–200.CrossRefGoogle Scholar
9.Hampshire, R.C. (2007). Dynamic queueing models for the operations management of communication services. Ph.D. dissertation, Princeton University, Princeton, NJ.Google Scholar
10.Hampshire, R.C., Massey, W.A., Mitra, D. & Wang, Q. (2002). Provisioning of bandwidth sharing and exchange. In Telecommunications network design and economics and management: Selected proceedings of the 6th INFORMS telecommunications conferences. Anandalingam, G. & Raghavan, S. (eds.). Boston: Kluwer Academic, pp. 207–226.Google Scholar
11.Jagerman, D.L. (1975). Nonstationary blocking in telephone traffic. Bell System Technical Journal 54: 625–661.CrossRefGoogle Scholar
12.Jennings, O.B., Mandelbaum, A., Massey, W.A. & Whitt, W. (1996). Server staffing to meet time-varying demand. Management Science 42(10): 1383–1394.CrossRefGoogle Scholar
13.Lanczos, C. (1970). The variational principles of mechanics, 4th ed.New York: Dover Publications.Google Scholar
14.Lanning, S.G., Massey, W.A., Rider, B. & Wang, Q. (1999). Optimal pricing in queuing systems with quality of service constraints. In Proceedings of the 16th International Teletraffic CongressEdinburgh, UK, pp. 747–756.Google Scholar
15.Mackie-Mason, J.F. & Varian, H. (1995). Pricing congestible network resources. IEEE Journal on Selected Areas in Communications 13(7): 1141–1149.CrossRefGoogle Scholar
16.Maglaras, C. & Zeevi, A. (2005). Pricing and design of differentiated services: approximate analysis and structural insights. Operations Research 53: 242–262.CrossRefGoogle Scholar
17.Massey, W.A. & Whitt, W. (1994). An analysis of the modified offered load approximation for the nonstationary Erlang loss model. Annals of Applied Probability 4(4): 1145–1160.CrossRefGoogle Scholar
18.Mendelson, H. (1985). Pricing computer services: queuing effects. Communications of the ACM 28(3): 312–321.CrossRefGoogle Scholar
19.Mendelson, H. & Whang, S. (1990). Optimal incentive-compatible priority pricing for the M/M/1 queue. Operations Research 38(5): 870–883.CrossRefGoogle Scholar
20.Pontryagin, L.S., Boltyanshii, V.G., Gamkredlidze, R.V. & Mishchenko, E.F. (1962). The mathematical theory of optimal processes. New York: Wiley.Google Scholar
21.Stidham, S. (1992). Pricing and capacity decisions for a service facility: stability and multiple local optima. Management Science 38(8): 1121–1139.CrossRefGoogle Scholar
22.Wang, Q., Peha, J.M. & Sirbu, M.A. (1997). Optimal pricing for integrated services networks. McKnight, L. W. & Bailey, J.P. (eds.), Internet Economics, Cambridge, MA: The MIT Press, pp. 352–376.Google Scholar
23.Westland, J.C. (1992). Congestion and network externalities in the short run pricing of information systems services. Management Science 38(6): 992–1099.CrossRefGoogle Scholar
24.Yoon, S. & Lewis, M.E. (2004). Optimal pricing and admission control in a queueing system with periodically varying parameters. Queueing Systems: Theory and Applications 47(3): 177–199.CrossRefGoogle Scholar