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Author: Zizhuo Wang
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
One of the major challenges in revenue management problems is the uncertainty of the customer's demand. In many practical problems, the decision maker can only observe realized demand over time, but not the underlying demand. Methods to incorporate demand learning into revenue management problems remain an important topic both for academic researchers and practitioners. In this dissertation, we propose a class of dynamic learning mechanisms for a broad range of revenue management problems. Unlike methods that attempt to deduce customer demand before the selling season, our proposed mechanisms enable a decision maker to learn the demand "on the fly''. That is, they integrate learning and doing into a concurrent procedure and both learning and doing are accomplished during the selling season. Furthermore, we show that our mechanisms are 1) Robust, that is, our mechanisms work well for a wide range of underlying demand structures and do not need to know the structure beforehand; 2) Dynamic, that is, our mechanisms take advantages of increasing sales data as selling season moves forward and update the demand information along the process and 3) Optimal, that is, our mechanisms achieve near-optimal revenues, as compared to a clairvoyant who has complete knowledge of the demand information beforehand. By comparing with different learning mechanisms, we claim that "dynamic learning" is essential for our mechanism to achieve improved performance and the above features. We consider two widely-used models in this dissertation, a customer bidding model and a posted-price model. For each model, we describe a dynamic learning mechanism with theoretical guarantee of near-optimal performance. Although different models entail different mechanism details and different analysis tools, we show that a common denominator is that the improvement of our mechanisms comes from one unique feature: dynamic learning. We also present numerical tests for both models from which the performance of our mechanisms is verified. We believe that the dynamic learning mechanisms presented in this dissertation have important implications for both researchers and practitioners.
Author: Zizhuo Wang
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
One of the major challenges in revenue management problems is the uncertainty of the customer's demand. In many practical problems, the decision maker can only observe realized demand over time, but not the underlying demand. Methods to incorporate demand learning into revenue management problems remain an important topic both for academic researchers and practitioners. In this dissertation, we propose a class of dynamic learning mechanisms for a broad range of revenue management problems. Unlike methods that attempt to deduce customer demand before the selling season, our proposed mechanisms enable a decision maker to learn the demand "on the fly''. That is, they integrate learning and doing into a concurrent procedure and both learning and doing are accomplished during the selling season. Furthermore, we show that our mechanisms are 1) Robust, that is, our mechanisms work well for a wide range of underlying demand structures and do not need to know the structure beforehand; 2) Dynamic, that is, our mechanisms take advantages of increasing sales data as selling season moves forward and update the demand information along the process and 3) Optimal, that is, our mechanisms achieve near-optimal revenues, as compared to a clairvoyant who has complete knowledge of the demand information beforehand. By comparing with different learning mechanisms, we claim that "dynamic learning" is essential for our mechanism to achieve improved performance and the above features. We consider two widely-used models in this dissertation, a customer bidding model and a posted-price model. For each model, we describe a dynamic learning mechanism with theoretical guarantee of near-optimal performance. Although different models entail different mechanism details and different analysis tools, we show that a common denominator is that the improvement of our mechanisms comes from one unique feature: dynamic learning. We also present numerical tests for both models from which the performance of our mechanisms is verified. We believe that the dynamic learning mechanisms presented in this dissertation have important implications for both researchers and practitioners.
Author: Xi Chen
Publisher: Springer Nature
ISBN: 3031019261
Category : Business & Economics
Languages : en
Pages : 444
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Book Description
This book examines recent developments in Operations Management, and focuses on four major application areas: dynamic pricing, assortment optimization, supply chain and inventory management, and healthcare operations. Data-driven optimization in which real-time input of data is being used to simultaneously learn the (true) underlying model of a system and optimize its performance, is becoming increasingly important in the last few years, especially with the rise of Big Data.
Author: David G. Luenberger
Publisher: Springer Nature
ISBN: 3030854507
Category : Business & Economics
Languages : en
Pages : 609
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Book Description
The 5th edition of this classic textbook covers the central concepts of practical optimization techniques, with an emphasis on methods that are both state-of-the-art and popular. One major insight is the connection between the purely analytical character of an optimization problem and the behavior of algorithms used to solve that problem. End-of-chapter exercises are provided for all chapters. The material is organized into three separate parts. Part I offers a self-contained introduction to linear programming. The presentation in this part is fairly conventional, covering the main elements of the underlying theory of linear programming, many of the most effective numerical algorithms, and many of its important special applications. Part II, which is independent of Part I, covers the theory of unconstrained optimization, including both derivations of the appropriate optimality conditions and an introduction to basic algorithms. This part of the book explores the general properties of algorithms and defines various notions of convergence. In turn, Part III extends the concepts developed in the second part to constrained optimization problems. Except for a few isolated sections, this part is also independent of Part I. As such, Parts II and III can easily be used without reading Part I and, in fact, the book has been used in this way at many universities. New to this edition are popular topics in data science and machine learning, such as the Markov Decision Process, Farkas’ lemma, convergence speed analysis, duality theories and applications, various first-order methods, stochastic gradient method, mirror-descent method, Frank-Wolf method, ALM/ADMM method, interior trust-region method for non-convex optimization, distributionally robust optimization, online linear programming, semidefinite programming for sensor-network localization, and infeasibility detection for nonlinear optimization.
Author: Sareh Nabi-Abdolyousefi
Publisher:
ISBN:
Category :
Languages : en
Pages : 83
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Book Description
The objective of this study is to propose novel dynamic pricing mechanisms in the presence of strategic customers using data-driven approaches. Dynamic pricing is the latest trend in pricing strategies and allows optimal response to real-time demand and supply information. Firms often face uncertainties when making pricing decisions. One of the uncertainties often involved is unknown demand. Therefore, businesses seek to optimize revenue while learning demand and reducing the uncertainty involved in setting prices. Understanding consumer decision-making is another crucial aspect of pricing in revenue management. One of the detrimental effects of dynamic pricing is that it invokes a type of behavior in customers that is referred to as forward-looking, or strategic, in revenue management literature. The strategic customer considers future price decreases, and purchases the product if his or her discounted surplus is higher than the immediate surplus. In chapters 1 and 2, we study a retailer who is pricing dynamically to maximize his expected cumulative revenue. We assume that the retailer has no information regarding expected demand nor the type of customers he is facing, whether they are myopic or strategic in their shopping behavior. In the problem of dynamic pricing under demand uncertainty, we face an inherent trade-off between the exploration involved in learning demand and the exploitation which occurs due to revenue maximization. One way of modeling this trade-off is using the multi-arm bandit modeling approach. Many algorithms have been proposed to solve stochastic multi-arm bandit problems. Our focus is on the Thompson Sampling (TS) algorithm which takes a Bayesian approach and was introduced by William R. Thompson. We propose a pricing mechanism called Strategic Thompson Sampling algorithm which is built upon the TS algorithm. Our main contribution in these two chapters is to merge the literature on strategic behavior with the literature on dynamic pricing and demand learning based on the classical multi-arm bandit modeling approach. In these chapters, the retailer is applying our proposed Strategic Thompson Sampling algorithm to learn expected demand in an exploration-versus-exploitation fashion. We start our analysis with a Bernoulli demand scenario in chapter 1 and extend our work to a Normal demand scenario in chapter 2. For both Bernoulli and Normal demand scenarios, we demonstrate numerically that the retailer's long run price offer decreases as the patience level of the strategic customer increases. We further show that the retailer can be better off in terms of his expected cumulative revenue when facing strategic customers. One potential explanation for this observation is the retailer's lower exploration of non-optimal arms in the presence of strategic customers rather than myopic ones. Our intuition is analytically and numerically confirmed for both Bernoulli and Normal demand scenarios. We further provide and compare expected regret bounds on the retailer's expected cumulative revenue for both types of customers. We conclude that the retailer's regret is lower when facing strategic customers as compared to myopic ones. Our objective in chapter 3 is to improve our starting point by building an informative prior and more specifically, an empirical Bayes prior for the Bayesian online learning algorithm that performs binary prediction. The underlying model used in this chapter is a Bayesian Linear Probit (BLIP) model which performs binary classification on a public data set called "Census Income Data Set". Our goal is to build an informative prior using a portion of the training data set and start the BLIP model with the built-in prior rather than the non-informative standard Normal distributions. We further compare the prediction accuracies of the BLIP model with informative and non-informative priors. An empirical Bayes model (Blip with empirical Bayes prior) has been implemented recently in the production system of one of the largest online retailers. The web-lab experiment is currently running.
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: Alex Gershkov
Publisher: MIT Press
ISBN: 0262552442
Category : Business & Economics
Languages : en
Pages : 209
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Book Description
A new approach to dynamic allocation and pricing that blends dynamic paradigms from the operations research and management science literature with classical mechanism design methods. Dynamic allocation and pricing problems occur in numerous frameworks, including the pricing of seasonal goods in retail, the allocation of a fixed inventory in a given period of time, and the assignment of personnel to incoming tasks. Although most of these problems deal with issues treated in the mechanism design literature, the modern revenue management (RM) literature focuses instead on analyzing properties of restricted classes of allocation and pricing schemes. In this book, Alex Gershkov and Benny Moldovanu propose an approach to optimal allocations and prices based on the theory of mechanism design, adapted to dynamic settings. Drawing on their own recent work on the topic, the authors describe a modern theory of RM that blends the elegant dynamic models from the operations research (OR), management science, and computer science literatures with techniques from the classical mechanism design literature. Illustrating this blending of approaches, they start with well-known complete information, nonstrategic dynamic models that yield elegant explicit solutions. They then add strategic agents that are privately informed and then examine the consequences of these changes on the optimization problem of the designer. Their sequential modeling of both nonstrategic and strategic logic allows a clear picture of the delicate interplay between dynamic trade-offs and strategic incentives. Topics include the sequential assignment of heterogeneous objects, dynamic revenue optimization with heterogeneous objects, revenue maximization in the stochastic and dynamic knapsack model, the interaction between learning about demand and dynamic efficiency, and dynamic models with long-lived, strategic agents.
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: 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: Aldo Romano
Publisher: Springer Science & Business Media
ISBN: 1441902511
Category : Computers
Languages : en
Pages : 192
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Book Description
Dynamic Learning Networks: Models and Cases in Action represents an attempt to provide a network perspective of organizational learning to drive dynamic competition through extended firm learning processes. This edited volume, contributed by worldwide experts in the field, provides academics and company managers with an extended view of organizational learning networks from real cases and different perspectives. Dynamic Learning Networks: Models and Cases in Action is based on the workshop, Managing Uncertainty and Competition through Dynamic Learning Networks. It was organized by the E-Business Management Section of Scuola Superiore ISUFI – University of Salento (Italy) – and held in Ostuni (Italy) in July 2008. Dynamic Learning Networks: Models and Cases in Action is designed for a professional audience, composed of researchers and practitioners working in corporate learning. This volume is also suitable for advanced-level students in computer science.
Author: Vasileios S. Zeimpekis
Publisher: Springer Science & Business Media
ISBN: 0387717226
Category : Business & Economics
Languages : en
Pages : 249
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Book Description
This book focuses on real time management of distribution systems, integrating the latest results in system design, algorithm development and system implementation to capture the state-of-the art research and application trends. The book important topics such as goods dispatching, couriers, rescue and repair services, taxi cab services, and more. The book includes real-life case studies that describe the solution to actual distribution problems by combining systemic and algorithmic approaches.
