New capacity allocation policies in revenue management

Official URL: http://risc01.sabanciuniv.edu/record=b1589785 (Table of Contents)

In this dissertation, we study three emerging problems in revenue management. First problem is about optimal capacity allocation in single-leg airline revenue management with overbooking. We propose new static and dynamic models. The static problems are difficult to solve optimally. Therefore, we introduce approximate models, which provide upper and lower bounds on the optimal expected revenues. In the dynamic case, we propose a model based on two streams of events; the arrivals of booking requests and cancellations. Following the characterization of the optimal policy, we also present the nested structure of the optimal allocations. Second problem is about optimal capacity allocation in the presence of a contingent commitment option. This option has been recently offered by airline systems to provide purchase flexibility to the customers. The problem becomes finding the revenue maximizing policy for contingent commitments and advance bookings. We first propose a dynamic programming model. Since it is computationally intractable, we develop an alternate dynamic model based on geometric approximation. In our numerical study, we investigate the effect of the commitment option on various test instances. In the third problem, we investigate optimal room allocation policies in hotel revenue management. Long-term stays are very common in hotel industry. Therefore, it is crucial to consider allocation of multiple-day capacities when responding to a request. This requirement leads to solving large-scale network problems, which are computationally challenging. Therefore, we work on various decomposition methods to find reservation policies for walk-in and stay-over customers. We also devise solution algorithms to solve large problems efficiently.