Distribution-free methods for multi-period, single-leg booking control

We study the multi-period, single-leg, fare class allocation problem in revenue management. Uncertainty in fare class demand is not characterized using probability distributions but by using lower and upper bounds on demand. Our multi-period model allows demand characteristics and available information to vary from period to period. Building on our previous work, static and dynamic models are developed to determine effective booking control policies based on competitive analysis of online algorithms. The underlying performance criterion used is a measure of robustness, which provides the best performance guarantee over all possible input sequences consistent with the data provided. The dynamic policies are nested by fare class in each period but the booking limits can be revised from period to period so that a fare class that is closed can be re-opened (if needed). Computational experiments compare the new policies against previously developed robust policies and also more traditional approaches. The experiments show that these new policies are effective, robust, and can provide significant gains over the existing policies.