Discrete dynamic pricing and application of network revenue management for FlixBus

We consider a real discrete pricing problem in network revenue management for FlixBus. We improve the company's current
pricing policy by an intermediate optimization step using booking limits from standard deterministic linear programs. We
pay special attention to computational efficiency. FlixBus' strategic decision to allow for low-cost refunds might encourage
large group bookings early in the booking process. In this context, we discuss counter-intuitive findings comparing booking
limits with static bid price policies. We investigate the theoretical question whether the standard deterministic linear program
for network revenue management does provide an upper bound on the optimal expected revenue if customer's willingness
to pay varies over time.