Dynamic pricing to control loss systems with quality of service targets

Authors:
Robert c. Hampshire;William a. Massey;Qiong Wang
Affiliations:
Carnegie mellon university, pittsburgh, pa e-mail: hamp@andrew.cmu.edu;Princeton university, princeton, nj e-mail: wmassey@princeton.edu;Bell laboratories, murray hill, nj e-mail: qwang@research.bell-labs.com
Venue:
Probability in the Engineering and Informational Sciences
Year:
2009

Citing 12
Cited 0

Pricing computer services: queueing effects

Communications of the ACM
Optimal incentive-compatible priority pricing for the M/M/1 queue

Operations Research
User delay costs and internal pricing for a service facility

Management Science
Pricing and capacity decisions for a service facility: stability and multiple local optima

Management Science
The physics of the Mt/G/ ∞ symbol Queue

Operations Research
Congestion and network externalities in the short run pricing of information system services

Management Science
Internet economics

Internet economics
Optimal pricing for integrated services networks

Internet economics
Optimal Pricing and Admission Control in a Queueing System with Periodically Varying Parameters

Queueing Systems: Theory and Applications
Pricing and Design of Differentiated Services: Approximate Analysis and Structural Insights

Operations Research
Optimality Of Randomized Trunk Reservation For A Problem With Multiple Constraints

Probability in the Engineering and Informational Sciences
Pricing congestible network resources

IEEE Journal on Selected Areas in Communications

Quantified Score

Hi-index	0.00

Visualization

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.