This dissertation studies yield management, i.e., how to sell a finite stock of perishable assets for maximum revenues. Here the assets can be goods, seats on a scheduled flight and hotel rooms, or capacity of a manufacturing facility. We consider two types of models: dynamic pricing models, in which only a single price is offered at any time, and multi-class models, in which the same assets can be sold at different prices. For a general dynamic pricing model, we show that the optimal price decreases in the number of items left, but does not necessarily decrease over time. The optimal price may go up when the reservation price distribution shifts up. We identify a sufficient condition under which the optimal price decreases over time. Numerical studies are used to show the revenue impact of price changes in compensating for demand statistical fluctuations and in exploiting shifts of customer reservation price. We study a multi-class dynamic model with non-homogeneous demand, which has useful applications in both service and manufacturing firms. The model assumes that a class can be reopened after being closed. We prove that the optimal policy is a monotone threshold policy. This structural property enables us to identify an analytical solution. Numerical examples show that the optimal dynamic policy outperforms the nave first-come-first-serve policies that are used by many manufacturers. However, the policies suggested by the above model are quite different from those suggested by the static models that are widely accepted by the airline industry. Therefore, we further propose a new dynamic multi-class model for airline seat allocation that does not allow reopening a class. This model incorporates passenger diversion and no-shows. We show that the optimal policy is a threshold policy, which may not be monotone. The optimal policy of this dynamic model is closely related to those from static models. Finally, we study a static multi-class model for airline seat allocation with overbooking. Under some mild conditions, we prove that a book-up-to policy is optimal. For the special case where no-show probability is homogeneous across all demand classes, a nested class policy is optimal.
Supervisor: Yu-Sheng Zheng. Thesis (Ph.D. in Operations and Information Management) -- University of Pennsylvania, 1999. Includes bibliographical references.