William A. Massey
Department of Operations Research and Financial Engineering
Princeton University

Abstract

Consider a real time service where arriving customers make their decision to join the system based on both the availability of resources and the current service price. Given a fixed number resources, the manager can use price as a mechanism to control the utilization of the system. A manager's goal is to find a pricing policy that maximizes total revenue while meeting the quality of service targets (i.e. the probability of congestion) desired by the customers.

Using variational calculus techniques, we solve the dynamic optimal control problem that results by approximating a dynamic rate, multi-server loss model as one with a constrained offered load. We do this by using customer demand forecasts to anticipate future service blocking and then implement a congestion pricing algorithm. 

This is joint work with Robert C. Hampshire of Carnegie Mellon University and Qiong Wang of Bell Labs.