A highly accurate demand forecast is fundamental to the success of every revenue management model. As is often required in both practice and theory, we aim to forecast the accumulated booking curve, as well as the number of reservations expected for each day in the booking horizon. To reduce the...

A highly accurate demand forecast is fundamental to the success of every revenue management model. As is often required in both practice and theory, we aim to forecast the accumulated booking curve, as well as the number of reservations expected for each day in the booking horizon. To reduce the...

In this paper we apply a new framework for the study of monotonicity in queueing systems to stochastic scheduling models. This allows us a unified treatment of many different models, among which are multiple and single server models (with and without feedback), discrete and continuous time...

In practice, call center service levels are reported over periods of finite length that are usually no longer than 24 hours. In such small periods the service level has a large variability. It is therefore not sufficient to base staffing decisions only on the expected service level. In this...

In this paper we apply a new framework for the study of monotonicity in queueing systems to stochastic scheduling models. This allows us a unified treatment of many different models, among which are multiple and single server models (with and without feedback), discrete and continuous time...

We consider Markov Decision Processes under light traffic conditions. We develop an algorithm to obtain asymptotically optimal policies for both the total discounted and the average cost criterion. This gives a general framework for several light traffic results in the literature. We illustrate...