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Author: Dirk Sierag
Publisher:
ISBN:
Category :
Languages : en
Pages : 26
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Book Description
This paper proposes and analyses a pricing-based revenue management model that allows flexible products on a network, with a non-trivial extension to group reservations. Under stochastic demand the problem can be solved using a dynamic programming formulation, though it suffers from the curse of dimensionality. The solution under deterministic demand gives an upper bound on the stochastic problem, and serves as a basis for two heuristics, which are asymptotically optimal. Numerical studies show that the heuristics perform well, even under uncertainty in demand. Moreover, neglecting flexible products can lead to substantial revenue loss.
Author: Dirk Sierag
Publisher:
ISBN:
Category :
Languages : en
Pages : 26
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Book Description
This paper proposes and analyses a pricing-based revenue management model that allows flexible products on a network, with a non-trivial extension to group reservations. Under stochastic demand the problem can be solved using a dynamic programming formulation, though it suffers from the curse of dimensionality. The solution under deterministic demand gives an upper bound on the stochastic problem, and serves as a basis for two heuristics, which are asymptotically optimal. Numerical studies show that the heuristics perform well, even under uncertainty in demand. Moreover, neglecting flexible products can lead to substantial revenue loss.
Author: Robert Phillips
Publisher: Stanford University Press
ISBN: 0804781648
Category : Business & Economics
Languages : en
Pages : 470
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Book Description
This is the first comprehensive introduction to the concepts, theories, and applications of pricing and revenue optimization. From the initial success of "yield management" in the commercial airline industry down to more recent successes of markdown management and dynamic pricing, the application of mathematical analysis to optimize pricing has become increasingly important across many different industries. But, since pricing and revenue optimization has involved the use of sophisticated mathematical techniques, the topic has remained largely inaccessible to students and the typical manager. With methods proven in the MBA courses taught by the author at Columbia and Stanford Business Schools, this book presents the basic concepts of pricing and revenue optimization in a form accessible to MBA students, MS students, and advanced undergraduates. In addition, managers will find the practical approach to the issue of pricing and revenue optimization invaluable. Solutions to the end-of-chapter exercises are available to instructors who are using this book in their courses. For access to the solutions manual, please contact
[email protected].
Author: Michael Müller-Bungart
Publisher: Springer Science & Business Media
ISBN: 3540723161
Category : Business & Economics
Languages : en
Pages : 307
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Book Description
This book analyzes revenue management (RM) problems with flexible products and RM in broadcasting companies. It presents models and methods that explicitly take the implications of flexibility into account. In addition, it contains descriptions of algorithms to generate stochastic demand data streams for general RM problems. To help readers with their own simulation studies, it provides an implementation as a Microsoft Windows executable file.
Author: Huiwen Jia
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
We consider a price-based network revenue management problem with multiple products and multiple reusable resources. Each randomly arriving customer requests a product (service) that needs to occupy a sequence of reusable resources (servers). We adopt an incomplete information setting where the firm does not know the price-demand function for each product and the goal is to dynamically set prices of all products to maximize the total expected revenue of serving customers. We propose novel batched bandit learning algorithms for finding near-optimal pricing policies, and show that they admit a near-optimal cumulative regret bound of $ tilde{O}(J sqrt{XT})$, where $J$, $X$, and $T$ are the numbers of products, candidate prices, and service periods, respectively. As part of our regret analysis, we develop the first finite-time mixing time analysis of an open network queueing system (i.e., the celebrated Jackson Network), which could be of independent interest. Our numerical studies show that the proposed approaches perform consistently well.
Author: Guillermo Gallego
Publisher: Springer
ISBN: 1493996061
Category : Business & Economics
Languages : en
Pages : 336
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Book Description
“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, Director of Pricing Research at Amazon “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, Research Scientist at Sabre “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.
Author: Sebastian Koch
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
We reconsider the stochastic dynamic program of revenue management with flexible products and customer choice behavior as proposed in the seminal paper by Gallego et al. [Gallego G, Iyengar G, Phillips R, Dubey A (2004) Managing flexible products on a network. Working paper, Columbia University, New York]. In the scientific literature on revenue management, as well as in practice, the prevailing strategy to operationalize dynamic programs is to decompose the network by resources and solve the resulting one-dimensional problems. However, up to now, these dynamic programming decomposition approaches have not been applicable to problems with flexible products, because the underlying state space is based on commitments rather than resources. In this paper, we contribute to the existing research by presenting an approach to operationalize revenue management with flexible products and customer choice in a dynamic programming environment. In particular, we propose a generic and formal procedure that transforms the original dynamic program with flexible products into an equivalent dynamic program with a resource-based state space. This reformulation renders the application of dynamic programming decomposition approaches possible. The procedure is based on Fourier-Motzkin elimination and is applicable to arbitrary network revenue management settings with regard to the considered network structure and the number and specifications of flexible products. Numerical experiments show a superior revenue performance of the new approach with average revenues close to the expected upper bound from the choice-based deterministic linear program (CDLP). Moreover, our reformulation improves revenues by up to 8% compared with an extended variant of a standard choice-based approach without flexible products that immediately assigns flexible products after sale.
Author: Kalyan T. Talluri
Publisher: Springer Science & Business Media
ISBN: 0387273913
Category : Business & Economics
Languages : en
Pages : 731
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Book Description
Revenue management (RM) has emerged as one of the most important new business practices in recent times. This book is the first comprehensive reference book to be published in the field of RM. It unifies the field, drawing from industry sources as well as relevant research from disparate disciplines, as well as documenting industry practices and implementation details. Successful hardcover version published in April 2004.
Author: Warren B. Powell
Publisher: John Wiley & Sons
ISBN: 0470182954
Category : Mathematics
Languages : en
Pages : 487
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Book Description
A complete and accessible introduction to the real-world applications of approximate dynamic programming With the growing levels of sophistication in modern-day operations, it is vital for practitioners to understand how to approach, model, and solve complex industrial problems. Approximate Dynamic Programming is a result of the author's decades of experience working in large industrial settings to develop practical and high-quality solutions to problems that involve making decisions in the presence of uncertainty. This groundbreaking book uniquely integrates four distinct disciplines—Markov design processes, mathematical programming, simulation, and statistics—to demonstrate how to successfully model and solve a wide range of real-life problems using the techniques of approximate dynamic programming (ADP). The reader is introduced to the three curses of dimensionality that impact complex problems and is also shown how the post-decision state variable allows for the use of classical algorithmic strategies from operations research to treat complex stochastic optimization problems. Designed as an introduction and assuming no prior training in dynamic programming of any form, Approximate Dynamic Programming contains dozens of algorithms that are intended to serve as a starting point in the design of practical solutions for real problems. The book provides detailed coverage of implementation challenges including: modeling complex sequential decision processes under uncertainty, identifying robust policies, designing and estimating value function approximations, choosing effective stepsize rules, and resolving convergence issues. With a focus on modeling and algorithms in conjunction with the language of mainstream operations research, artificial intelligence, and control theory, Approximate Dynamic Programming: Models complex, high-dimensional problems in a natural and practical way, which draws on years of industrial projects Introduces and emphasizes the power of estimating a value function around the post-decision state, allowing solution algorithms to be broken down into three fundamental steps: classical simulation, classical optimization, and classical statistics Presents a thorough discussion of recursive estimation, including fundamental theory and a number of issues that arise in the development of practical algorithms Offers a variety of methods for approximating dynamic programs that have appeared in previous literature, but that have never been presented in the coherent format of a book Motivated by examples from modern-day operations research, Approximate Dynamic Programming is an accessible introduction to dynamic modeling and is also a valuable guide for the development of high-quality solutions to problems that exist in operations research and engineering. The clear and precise presentation of the material makes this an appropriate text for advanced undergraduate and beginning graduate courses, while also serving as a reference for researchers and practitioners. A companion Web site is available for readers, which includes additional exercises, solutions to exercises, and data sets to reinforce the book's main concepts.
Author: Robert L. Phillips
Publisher: Stanford Business Books
ISBN: 9781503610002
Category : Business & Economics
Languages : en
Pages : 0
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Book Description
Background -- Introduction to pricing and revenue optimization -- Models of demand -- Estimating price response -- Optimization -- Price differentiation -- Pricing with constrained supply -- Revenue management -- Capacity allocation -- Network management -- Overbooking -- Markdown management -- Customized pricing -- Behavioral economics and pricing.
Author: Wang Chi Cheung
Publisher:
ISBN:
Category :
Languages : en
Pages : 57
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Book Description
We consider the choice-based network revenue management problem (NRM). The LP relaxation, Choice-based Deterministic Linear Program (CDLP-P), has been proposed to mitigate the curse of dimensionality of the choice-based NRM. Despite the importance of CDLP-P, there is no polynomial time algorithm for solving CDLP-P for general choice models. Moreover, most heuristics for solving CDLP-P require solving the underlying Single Period Problem (SPP), which could be NP-hard. We propose the Potential Based algorithm (PB) that solves CDLP-P to near optimality with provable efficiency, assuming the ability to solve the underlying SPP approximately. In particular, PB implies polynomial time algorithms for approximating CDLP-P for a variety of choice models, such as Nested Logit, Mixed Logit and Markov Chain models, to near optimality.We also propose the Approximate Column Generation heuristic (ACG), which generalizes the classical Column Generation heuristic, and returns a near optimal solution to CDLP-P at termination. Different from PB, ACG is not known to be provably efficient. Finally, building on the tractability result, we design an earning-while-learning policy for the online NRM problem under an MultiNomial Logit choice model with unknown parameters. The policy runs in polynomial time, and achieves a sublinear regret.
