Efficiency and Performance Guarantees for Choice-Based Network Revenue Management Problems with Flexible Products PDF Download
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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.
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.
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: 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: Arne Karsten Strauss
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: He Wang (Ph. D.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 157
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Book Description
With the advances in information technology and the increased availability of data, new approaches that integrate learning and decision making have emerged in operations management. The learning-and-optimizing approaches can be used when the decision maker is faced with incomplete information in a dynamic environment. We first consider a network revenue management problem where a retailer aims to maximize revenue from multiple products with limited inventory constraints. The retailer does not know the exact demand distribution at each price and must learn the distribution from sales data. We propose a dynamic learning and pricing algorithm, which builds upon the Thompson sampling algorithm used for multi-armed bandit problems by incorporating inventory constraints. Our algorithm proves to have both strong theoretical performance guarantees as well as promising numerical performance results when compared to other algorithms developed for similar settings. We next consider a dynamic pricing problem for a single product where the demand curve is not known a priori. Motivated by business constraints that prevent sellers from conducting extensive price experimentation, we assume a model where the seller is allowed to make a bounded number of price changes during the selling period. We propose a pricing policy that incurs the smallest possible regret up to a constant factor. In addition to the theoretical results, we describe an implementation at Groupon, a large e-commerce marketplace for daily deals. The field study shows significant impact on revenue and bookings. Finally, we study a supply chain risk management problem. We propose a hybrid strategy that uses both process flexibility and inventory to mitigate risks. The interplay between process flexibility and inventory is modeled as a two-stage robust optimization problem: In the first stage, the firm allocates inventory, and in the second stage, after disruption strikes, the firm schedules its production using process flexibility to minimize demand shortage. By taking advantage of the structure of the second stage problem, we develop a delayed constraint generation algorithm that can efficiently solve the two-stage robust optimization problem. Our analysis of this model provides important insights regarding the impact of process flexibility on total inventory level and inventory allocation pattern.
Author: Institute for Operations Research and the Management Sciences. National Meeting
Publisher:
ISBN:
Category : Industrial management
Languages : en
Pages : 480
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Book Description
Author: Stanislav Ivanov
Publisher: Zangador
ISBN: 9549278638
Category : Travel
Languages : en
Pages : 205
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Book Description
This research monograph aims at developing an integrative framework of hotel revenue management. It elaborates the fundamental theoretical concepts in the field of hotel revenue management like the revenue management system, process, metrics, analysis, forecasting, segmentation and profiling, and ethical issues. Special attention is paid on the pricing and non-pricing revenue management tools used by hoteliers to maximise their revenues and gross operating profit. The monograph investigates the revenue management practices of accommodation establishments in Bulgaria and provides recommendations for their improvement. The book is suitable for undergraduate and graduate students in tourism, hospitality, hotel management, services studies programmes, and researchers interested in revenue/yield management. The book may also be used by hotel general managers, marketing managers, revenue managers and other practitioners looking for ways to improve their knowledge in the field.
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: Tudor Bodea
Publisher: Routledge
ISBN: 1136624848
Category : Business & Economics
Languages : en
Pages : 267
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Book Description
The practices of revenue management and pricing analytics have transformed the transportation and hospitality industries, and are increasingly important in industries as diverse as retail, telecommunications, banking, health care and manufacturing. Segmentation, Revenue Management and Pricing Analytics guides students and professionals on how to identify and exploit revenue management and pricing opportunities in different business contexts. Bodea and Ferguson introduce concepts and quantitative methods for improving profit through capacity allocation and pricing. Whereas most marketing textbooks cover more traditional, qualitative methods for determining customer segments and prices, this book uses historical sales data with mathematical optimization to make those decisions. With hands-on practice and a fundamental understanding of some of the most common analytical models, readers will be able to make smarter business decisions and higher profits. This book will be a useful and enlightening read for MBA students in pricing and revenue management, marketing, and service operations.
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.
