This book is divided into three parts: Traditional revenue management, revenue management under customer choice, and pricing analytics. Each chapter includes an appendix including proofs of some important technical results, and each also includes end-of-chapter problems or questions. The first part of the book, on traditional revenue management, has a rigorous treatment of the most important models developed in industry where time is treated implicitly and those developed in academia where time is considered explicitly. Coverage includes Littlewood's rule, bounds and heuristics for single resource and network models, overbooking, and some extensions such as the generalized newsvendor problem with convex costs.The second part of the book, on revenue management under customer choice, is a modern treatment of revenue management that largely gives up on Littlewood's rule and its extensions in favor of a dynamic programming framework based on discrete choice models and the machinery of assortment optimization. Bounds, heuristics, and asymptotic properties are presented. This section starts with a chapter on discrete choice models, followed by a chapter on assortment optimization as these are the foundation of revenue management models under customer choice.The third part of the book is on pricing analytics. It starts with an in-depth treatment of pricing models with linear costs (that arise as dual prices of capacity constraints), and a rigorous treatment of the necessary and sufficient conditions for existence and uniqueness of optimal prices. The authors show that firms prefer input costs that are random, and that consumers also prefer random prices. They establish conditions on the pass-through rate under which randomness in inputs for the firm benefit both the firm and the consumer. They also provide treatments of a number of extensions including call options on capacity and models that allow for bargaining and concise treatments of the peak-load pricing and priority pricing. The chapter on dynamic pricing extends known formulations to allow for inventory replenishments, holding costs, discounted costs, compound Poisson demands and dynamic non-linear pricing. The chapter on on-line learning presents some of the most relevant results in learning and earning, while the final chapter on competition presents models relevant to revenue management and dynamic pricing.weiterlesen