Many industries use revenue management to balance uncertain, stochastic demand and inflexible capacity. Popular examples include airlines, hotels, car rentals, retailing, and manufacturing. The classical revenue management approaches considered in theory and practice are based on two...

Revenue management models traditionally assume that future demand is unknown but can be described by a stochastic process or a probability distribution. Demand is, however, often difficult to characterize, especially in new or nonstationary markets. In this paper, we develop robust formulations...

We propose and analyze a sequential design of price experimentation that balances the learning and earning trade-off in revenue management. Assuming the demand function belongs to a parametric family with an unknown parameter value, we derive a closed-form stopping rule based on the realized...

Robust revenue management has received much interest from both academics and practitioners alike. But the airlines have not embraced it for its current inadequacy to address the practical needs for both fine control of conservatism and the adaptability to changing environments. The single-leg...

Model risk as part of the operational risk is a serious problem for financial institutions. As the pricing of derivatives as well as the computation of the market or credit risk of an institution depend on statistical models the application of a wrong model can lead to a serious over- or...