Journal ArticleDOI
An Improved Dynamic Programming Decomposition Approach for Network Revenue Management
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TLDR
This work considers a nonlinear nonseparable functional approximation to the value function of a dynamic programming formulation for the network revenue management (RM) problem with customer choice and shows that it leads to a tighter upper bound on optimal expected revenue than some known bounds in the literature.Abstract:
We consider a nonlinear nonseparable functional approximation to the value function of a dynamic programming formulation for the network revenue management (RM) problem with customer choice. We propose a simultaneous dynamic programming approach to solve the resulting problem, which is a nonlinear optimization problem with nonlinear constraints. We show that our approximation leads to a tighter upper bound on optimal expected revenue than some known bounds in the literature. Our approach can be viewed as a variant of the classical dynamic programming decomposition widely used in the research and practice of network RM. The computational cost of this new decomposition approach is only slightly higher than the classical version. A numerical study shows that heuristic control policies from the decomposition consistently outperform policies from the classical decomposition.read more
Citations
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Journal ArticleDOI
Robust Assortment Optimization in Revenue Management Under the Multinomial Logit Choice Model
TL;DR: This work gives a complete characterization of the optimal policy in both settings, shows that it can be computed efficiently, and derive operational insights, and proposes a family of uncertainty sets that enables the decision maker to control the trade-off between increasing the average revenue and protecting against the worst-case scenario.
Journal ArticleDOI
A review of choice-based revenue management: Theory and methods
TL;DR: This paper describes recent developments on tackling the entire control problem by reviewing the key literature on choice-based revenue management, specifically focusing on methodological publications of availability control over the years 2004–2017.
Journal ArticleDOI
Dynamic Capacity Management with General Upgrading.
TL;DR: It is found that under the proposed heuristic, the value of using sophisticated multistep upgrading can be quite significant; however, using simple approximations for the initial capacity leads to negligible profit loss, which suggests that the firm’s profit is not sensitive to theInitial capacity decision if the optimal upgrading policy is used.
Journal ArticleDOI
Dynamic Pricing for Network Revenue Management: A New Approach and Application in the Hotel Industry
Dan Zhang,Larry Weatherford +1 more
TL;DR: A large-scale numerical study uses data obtained from a major hotel to compare the performance of several heuristic approaches proposed in the literature and offers a cautionary tale on the choice of heuristic methods for practical network pricing problems.
Journal ArticleDOI
Hotel chains: survival strategies for a dynamic future.
TL;DR: In this paper, a literature review consisting of current events, industry reports, and recent trends is utilized to summarize and categorize the challenges and opportunities facing hotel chains, and a comprehensive set of recommendations to hotel chains highlighting opportunities related to: financing, revenue generation, personalization, and co-creation.
References
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Book
Markov Decision Processes: Discrete Stochastic Dynamic Programming
TL;DR: Puterman as discussed by the authors provides a uniquely up-to-date, unified, and rigorous treatment of the theoretical, computational, and applied research on Markov decision process models, focusing primarily on infinite horizon discrete time models and models with discrete time spaces while also examining models with arbitrary state spaces, finite horizon models, and continuous time discrete state models.
Neuro-Dynamic Programming.
TL;DR: In this article, the authors present the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control.
Book
Neuro-dynamic programming
TL;DR: This is the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control.
BookDOI
Approximate dynamic programming : solving the curses of dimensionality
TL;DR: This book discusses the challenges of dynamic programming, the three curses of dimensionality, and some experimental comparisons of stepsize formulas that led to the creation of ADP for online applications.
Book
The Theory and Practice of Revenue Management
TL;DR: In this article, the authors present the economics of RM, including single-resource capacity control, network capacity control and overbooking, as well as dynamic pricing and auctioning.