• Addresses the gap between the latest research and established texts in the field - Looks at research in Economics, Marketing, Operations, and Psychology - Strives for readability, with most difficult proofs in Appendix

Autorentext
Guillermo Gallego is the Department Head of Industrial Engineering and Decision Analytics, and also the Crown Worldwide Professor of Engineering. Prior to his appointment in January 2016, Prof Gallego was the Liu Family Professor at the Department of Industrial Engineering and Operations Research at Columbia University, where he served as the Department Chairman from 2002-2008. He was named an INFORMS Fellow in 2012, a Manufacturing and Service Operations Management Society (MSOM) Distinguished Fellow in 2013 and has been the recipient of many awards including the Revenue Management Historical Prize (2011), the Revenue Management Practice Prize (2012), the INFORMS Impact Prize (2016) and the Management Science Best Paper Award (2017). Prof Gallego's research interests are Dynamic Pricing and Revenue Optimization, Supply Chain Management, Electronic Commerce, and Inventory Theory. He has published influential papers in the leading journals of his field where he has also occupied a variety of editorial positions. His work has been supported by numerous industrial and government grants. In addition to theoretical research, Prof Gallego has developed strong collaboration with global corporations such as Disney World, Hewlett Packard, IBM, Lucent Technologies, Nomis Solutions, and Sabre Airline Solutions. He has also worked with government agencies such as the National Research Council, the National Science Foundation in United States and the Ireland Development Agency. His graduate students are associated with prestigious universities and occupy leading roles in their chosen fields. He spent his 1996-97 sabbatical at Stanford University and was a visiting scientist at the IBM Watson Research Center from 1999-2003. Prof Gallego received both his PhD degree (1988) and MS degree (1987) in Operations Research and Industrial Engineering from Cornell University, and his undergraduate degree in Mathematics (1980) from the University of California in San Diego.
Huseyin Topaloglu is a professor of operations research in the School of Operations Research and Information Engineering at Cornell University. He joined Cornell in 2002. After working on Cornell's main campus in Ithaca for 13 years, he moved to Cornell's technology-focused campus in New York City in 2015. Professor Topaloglu's research interests are in stochastic optimization and approximation algorithms with applications in revenue management, dynamic pricing and transportation logistics. He serves on the editorial boards of Management Science, Mathematical Programming C, Naval Research Logistics, Operations Research, and Production and Operations Management. He teaches courses on optimization theory, revenue management, and logistics.

Klappentext

There is no strategic investment that has a higher return than investing in good pricing, and the text by Gallego and Topaloghu provides the best technical treatment of pricing strategy and tactics available. Preston McAfee, the J. Stanley Johnson Professor, California Institute of Technology and Chief Economist and Corp VP, Microsoft.

The book by Gallego and Topaloglu provides a fresh, up to date and in depth treatment of revenue management and pricing. It fills an important gap as it covers not only traditional revenue management topics also new and important topics such as revenue management under customer choice as well as pricing under competition and online learning. The book can be used for different audiences that range from advanced undergraduate students to masters and PhD students. It provides an in depth treatment covering recent state of the art topics in an interesting and innovative way. I highly recommend it. Professor Georgia Perakis, the William F. Pounds Professor of Operations Research and Operations Management at the Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts. 

This book is an important and timely addition to the pricing analytics literature by two authors who have made major contributions to the field.   It covers traditional revenue management as well as assortment optimization and dynamic pricing.  The comprehensive treatment of choice models in each application is particularly welcome.  It is mathematically rigorous but accessible to students at the advanced undergraduate or graduate levels with a rich set of exercises at the end of each chapter.  This book is highly recommended for Masters or PhD level courses on the topic and is a necessity for researchers with an interest in the field. Robert L. Phillips, Amazon

This book, written by two of the leading researchers in the area, brings together in one place most of the recent research on revenue management and pricing analytics.    New industries (ride sharing, cloud computing, restaurants) and new developments in the airline and hotel industries make this book very timely and relevant, and will serve as a critical reference for researchers. Professor Kalyan Talluri, the Munjal Chair in Global Business and Operations, Imperial College, London, UK. 

At last, a serious and comprehensive treatment of modern revenue management and assortment optimization integrated with choice modeling.  In this book, Gallego and Topaloglu provide the underlying model derivations together with a wide range of applications and examples; all of these facets will better equip students for handling real-world problems.  For mathematically inclined researchers and practitioners, it will doubtless prove to be thought-provoking and an invaluable reference. - Richard Ratliff** **

Inhalt

Chapter 1. Single Resource Revenue Management with Independent Demands.- Chapter 2. Network Revenue Management with Independent Demands.- Chapter 3. Overbooking.- Chapter 4. Introduction to Choice Modeling.- Chapter 5. Assortment Optimization.- Chapter 6. Single Resource Revenue Management with Dependent Demands.- Chapter 7. Network Revenue Management with Dependent Demands.- Chapter 8. Basic Pricing Theory.- Chapter 9. Dynamic Pricing over Finite Horizons.- Chapter 10. Online Learning.- Chapter 11. Competitive Assortment and Price Optimization.