Economics Insights @ CBS

We study a capacity sizing problem in a service system that is modeled as a single-class queue with multiple servers and where customers may renege while waiting for service. A salient feature of the model is that the mean arrival rate of work is random (in practice this is a typical consequence of forecasting errors). The paper elucidates the impact of uncertainty on the nature of capacity prescriptions, and relates these to well established rules-of-thumb such as the square root safety staffing principle.

We study scheduling of multimedia traffic on the downlink of a wireless communication system. We examine a scenario where multimedia packets are associated with strict deadlines and are equivalent to lost packets if they arrive after their associated deadlines. Lost packets result in degradation of playout quality at the receiver, which is quantified in terms of the "distortion cost" associated with each packet. Our goal is to design a scheduler which minimizes the aggregate distortion cost over all receivers. We study the scheduling problem in a dynamic programming (DP) framework.

Discrete choice models are appealing for airline revenue management (RM) because they offer a means to profitably exploit preferences for attributes such as time of day, routing, brand, and price. They are also good at modeling demand for unrestricted fare class structures, which are widespread throughout the industry. However, there is little empirical research on the practicality and effectiveness of choice-based RM models. Toward this end, we report the results of a study of choice-based RM conducted with a major U.S. airline.