Learning and optimizing through dynamic pricing

Many airlines have been actively looking into class-free inventory control approaches, in which the control policy consists of dynamically varying prices over a continuous interval rather than opening and closing fare classes. As evidenced both in literature and in practice, one of the big challenges in this setting is the trade-off between policies that learn the demand parameters quickly and those that maximize expected revenue. Starting in a typical single-leg airline revenue management context, we investigate the applicability of recent advances in the area of optimal control with learning. We consider a demand model where customers’ maximum willingness-to-pay has a Gaussian distribution and we analyze several estimation and pricing approaches that include the expectation–maximization and a scheme of active generation of price variability. We show that our model ensures discovery of the underlying customer behavior while providing an appropriate level of expected revenue via a simulated example.