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2019 | Book

Single Resource Revenue Management with Independent Demands Read chapter Read first chapter

Revenue Management and Pricing Analytics

Authors: Guillermo Gallego, Huseyin Topaloglu

Publisher: Springer New York

Book Series : International Series in Operations Research & Management Science

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

“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

Table of Contents

Frontmatter

Traditional Revenue Management

Frontmatter
Chapter 1. Single Resource Revenue Management with Independent Demands
Abstract
In this chapter, we consider the single resource, independent demand revenue management problem with multiple fare classes. This problem arises in the airline industry where different fares for the same cabin are designed to cater to different market segments. As an example, a low fare may have advance purchase and length of stay restrictions and exclude ancillary services such as advance seat selection, luggage handling, and priority boarding. This low fare may target price-conscious consumers who travel for leisure on restricted budgets. On the other hand, a high fare designed for business consumers may be unrestricted, include ancillary services and be designed to be frequently available for late bookings. If requests for the low fare arrive first, the airline risks selling all of its capacity before seeing requests for the high fare. A key decision in revenue management is how much capacity to reserve for higher fare classes, or equivalently how much capacity to make available for lower fare classes. Throughout the chapter, we will refer to airline applications, but the reader should keep in mind that the models apply more generally.
Guillermo Gallego, Huseyin Topaloglu
Chapter 2. Network Revenue Management with Independent Demands
Abstract
In this chapter, we consider a firm that has finite capacities of several resources that can be instantly combined into different products with fixed prices. We assume that there is an independent demand stream for each of the products that arrives as a Poisson process. A requested product is purchased if available. The firm generates the revenue associated with the sale and updates the inventories of the resources consumed by the product. If the requested product is not available, then the customer leaves the system without purchasing. The objective of the firm is to decide which products to make available over a finite sales horizon to maximize the total expected revenue from fixed initial inventories that cannot be replenished during the sales horizon.
Guillermo Gallego, Huseyin Topaloglu
Chapter 3. Overbooking
Abstract
Early on, many airlines adopted the policy of not penalizing booked customers for canceling reservations at any time before departure. Some would not even penalize those that did not show up for booked flights. In essence, an airline ticket was “like money” since it could be used at full face value for a future flight or redeemed for cash at any future date. In the sixties, no-shows were becoming a problem for airlines who found that flights that were fully booked were departing with many empty seats. In response, the airlines began to overbook as a means of hedging against no-shows. If a flight had more passengers show than there were seats available, then the airlines would bump some passengers. The bumped passengers would be re-booked on a later flight. In addition, bumped passengers would be given other compensation, often a meal at the airport and a discount certificate applicable to future travel. The cost to the airline of bumping a passenger is called the denied boarding cost. The denied boarding cost would include the cost of putting a bumped passenger on another flight to her destination, the cost of any direct compensation to the bumped passenger, the cost of the meals or lodging that the airline provides to each bumped passenger, and the cost of “ill will” incurred by bumping the passenger. These costs can be different for each flight. For example, a passenger bumped from the last flight of the day will be provided with a hotel room at the airline’s expense.
Guillermo Gallego, Huseyin Topaloglu

Revenue Management Under Customer Choice

Frontmatter
Chapter 4. Introduction to Choice Modeling
Abstract
Revenue management models were originally developed under the assumption of stochastically independent demands. This assumption is untenable when products are close substitutes. In this case, the demand for a particular product may depend on the set of competing products that are available in the market. For example, when a product is removed from an assortment, its demand may be recaptured by another product in the assortment, or it may spill to competitors or the no-purchase alternative.
Guillermo Gallego, Huseyin Topaloglu
Chapter 5. Assortment Optimization
Abstract
A fundamental question in revenue management involves deciding which fares to offer in response to a request from an origin to a destination. The solution depends on available capacity, the time of departure, and how consumers make choices. This problem needs to be solved in real time as travel requests arrive. The assortment optimization problem is crucial in other RM applications such as hotels and car rentals, and is becoming more important in retailing and e-commerce. The fundamental tradeoff in assortment optimization is that broad assortments result in demand cannibalization and spoilage, while narrow assortments result in disappointed consumers that may walk away without purchasing. The formulation can be interpreted broadly to include more strategic decisions such as the location of stores within a city. The profitability of an assortment can be best captured through a choice model that provides sale probabilities as a function of the set of products contained in the assortment. In this chapter, we formulate and solve assortment optimization problems for many of the choice models presented in the previous chapter.
Guillermo Gallego, Huseyin Topaloglu
Chapter 6. Single Resource Revenue Management with Dependent Demands
Abstract
Revenue managers struggled for decades with the problem of finding optimal control mechanisms for fare class structures with dependent demands. In this context, a resource, such as seats on a plane, can be offered at different fares with potentially different restrictions and ancillary services, and the demand for those fares is interdependent. The question is what subset of the fares (or assortment of products) to offer for sale at any given time. Practitioners often use the term open, or open for sale, for a fare that is part of the offered assortment, and the term closed for fares that are not part of the offered assortment. For many years, practitioners preferred to model time implicitly by seeking extensions of Littlewood’s rule and EMSR type heuristics to the case of dependent demands. Finding the right way to extend Littlewood’s rule proved to be more difficult than anticipated. An alternative approach, favored by academics and gaining traction in industry, is to model time explicitly. In this chapter, we will explore both formulations but most of our attention is devoted to the more tractable model where time is treated explicitly.
Guillermo Gallego, Huseyin Topaloglu
Chapter 7. Network Revenue Management with Dependent Demands
Abstract
Network revenue management models have traditionally been developed under the independent demand assumption. In the independent demand setting, customers arrive into the system with the intention to purchase a particular product. If this product is available, they purchase it. Otherwise, they leave the system. This model is reasonable when products are well differentiated so that customers do not substitute between products. The independent demand model is harder to justify when there are few differences, other than price, between fares. Indeed, a more general setting is needed when the demand for each product depends heavily on whether or not other products are available for sale. This setting gives the firms the opportunity to shape the demand for each product by adjusting the offer set made available to the customer.
Guillermo Gallego, Huseyin Topaloglu

Pricing Analytics

Frontmatter
Chapter 8. Basic Pricing Theory
Abstract
This chapter provides an introduction to multi-product monopoly pricing when the variable costs are linear. Profit maximization problems with linear variable costs arise from capacity constraints, where the firm maximizes the expected profit net of the opportunity costs of the capacities used. We argue that under mild assumptions, both the optimal profit function and the expected consumer surplus are convex functions of the variable costs. Consequently, when variable costs are random, both the firm and the representative consumer benefit from prices that dynamically respond to changes in variable costs. Randomness in variable cost is often driven by randomness in demand in conjunction with capacity constraints, and this accounts for some of the benefits of dynamic pricing. We explore conditions for the existence and uniqueness of maximizers of the expected profit and analyze in detail problems with capacity constraints both when prices are set for the entire sales horizon a priori, and when prices are allowed to change during the sales horizon. The firm’s problem is discussed in Sect. 8.2, while the representative consumer’s problem is presented in Sect. 8.3. The case with finite capacity is discussed in Sect. 8.4. Details about existence and uniqueness for single product problems are discussed in Sect. 8.5. This section also includes applications to priority pricing, social planning, multiple market segments, and peak-load pricing. Multi-product pricing problems are discussed in Sect. 8.6.
Guillermo Gallego, Huseyin Topaloglu
Chapter 9. Dynamic Pricing Over Finite Horizons
Abstract
In this chapter, we first consider the problem of dynamically pricing one or more products that consume a single resource. Sales take place over a finite selling horizon, and the objective is to maximize the expected revenue that can be obtained from a finite inventory of the resource. We will assume that inventories cannot be replenished during the sales horizon. This problem setup holds for hotels, airlines, and seasonal merchandise including fashion retailing that have long procurement lead times. In this chapter, we focus on models that explicitly consider the stochastic and dynamic nature of demand. We use dynamic programming formulations to compute an optimal policy as a function of the remaining inventory and the time-to-go. In some cases, we are able to give closed-form solutions for the value function and the optimal pricing policy. In other cases, we resort to numerical solutions and to heuristic policies.
Guillermo Gallego, Huseyin Topaloglu
Chapter 10. Online Learning
Abstract
In the models that we have studied so far, we have assumed that the demand model and its parameters are all known. In practice, demand models need to be estimated before dynamic pricing, assortment optimization, and revenue management can be effectively done. In some instances, there is enough data over a long period of time to calibrate different demand models, do model selection, and update parameter estimates. At the other extreme, we may be pricing for products for which we have little or no information. In this case, demand learning needs to be done on the fly. This is particularly true for online retailing of new products. In this chapter, we address the problem of online demand learning. We study the expected loss in revenue of a learning-and-earning policy relative to an optimal clairvoyant policy that knows the expected demand function. We consider both the case of ample and constrained capacity and measure how the regret grows as the length of the sales horizon increases. We present only the strongest available results for both the case of ample and the case of constrained capacity. In Sect. 10.2, we consider the case with ample capacity, whereas in Sect. 10.3, we consider the case with constrained capacity.
Guillermo Gallego, Huseyin Topaloglu
Chapter 11. Competitive Assortment and Price Optimization
Abstract
In the models that we studied thus far, we considered the decisions made by a single firm. The implicit assumption in our development was that the other firms do not react to the decisions of each other. Naturally, this is almost never the case. When a firm decreases its prices, fearing loss of customers, its competitors may also decrease its prices. Both online and brick-and-mortar retail stores consider the assortments offered by the other stores when making planning their assortments. There is vast literature on modeling competition. Nevertheless, despite the fact that competition is the rule rather than an exception and there is vast literature on modeling competition, the development of operational models that can drive real-time decision making under competition is in its infancy. In most operational models, it is often the case that the competition is ignored or modeled rather simplistically. Perhaps, the most important reason for this is that explicitly modeling competition often times results in intractable models. Thus, for the sake of computational tractability, the reactions of the other firms are ignored. Furthermore, the data that drive the operational models are often collected in a competitive environment, and one usually naively hopes that building a noncompetitive model driven by data collected in a competitive environment will take care of the competition itself, but of course, this hope is not based on any scientific evidence.
Guillermo Gallego, Huseyin Topaloglu
Backmatter
Metadata
Title
Revenue Management and Pricing Analytics
Authors
Guillermo Gallego
Huseyin Topaloglu
Copyright Year
2019
Publisher
Springer New York
Electronic ISBN
978-1-4939-9606-3
Print ISBN
978-1-4939-9604-9
DOI
https://doi.org/10.1007/978-1-4939-9606-3