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Author: J. Dai Publisher: ISBN: Category : Languages : en Pages : 41
Book Description
This paper describes a revenue management problem of a major airline that operates in a very competitive market involving two major hubs and having more than 30 parallel daily flights. We consider choice based stochastic assortment optimization problems to maximize the expected revenue for the airline. The inputs include models of booking arrival rates, competitor assortment selection probabilities, customers' booking choices among the airline's own flights as well as competitors' flights, booking-to-ticketing conversion probabilities, and go-show and no-show probabilities. We build a variety of booking choice models to incorporate unobserved heterogeneous customer preferences for different departure times. The way departure time preferences are modeled dramatically affects price sensitivity estimates, and therefore the modeling of heterogeneous departure time preferences matters. We also show that customer choice behavior exhibits discontinuities, with much greater demand for the cheapest alternative than for the second cheapest alternative even when the price difference is small, and much greater demand for fully refundable tickets than almost fully refundable tickets. We formulate a deterministic (fluid) optimization problem corresponding to each of the booking choice models, and we show that in some cases these problems can be solved efficiently even when the discontinuities cause violation of the independence from irrelevant alternatives property. The resulting solutions are used to determine assortment selection policies for the stochastic problem. Simulation studies show that several of these policies generate significantly more revenue than the airline's existing policy, and that the improved performance of these policies is robust with respect to misspecification errors as well as with respect to errors in parameter estimates.
Author: J. Dai Publisher: ISBN: Category : Languages : en Pages : 41
Book Description
This paper describes a revenue management problem of a major airline that operates in a very competitive market involving two major hubs and having more than 30 parallel daily flights. We consider choice based stochastic assortment optimization problems to maximize the expected revenue for the airline. The inputs include models of booking arrival rates, competitor assortment selection probabilities, customers' booking choices among the airline's own flights as well as competitors' flights, booking-to-ticketing conversion probabilities, and go-show and no-show probabilities. We build a variety of booking choice models to incorporate unobserved heterogeneous customer preferences for different departure times. The way departure time preferences are modeled dramatically affects price sensitivity estimates, and therefore the modeling of heterogeneous departure time preferences matters. We also show that customer choice behavior exhibits discontinuities, with much greater demand for the cheapest alternative than for the second cheapest alternative even when the price difference is small, and much greater demand for fully refundable tickets than almost fully refundable tickets. We formulate a deterministic (fluid) optimization problem corresponding to each of the booking choice models, and we show that in some cases these problems can be solved efficiently even when the discontinuities cause violation of the independence from irrelevant alternatives property. The resulting solutions are used to determine assortment selection policies for the stochastic problem. Simulation studies show that several of these policies generate significantly more revenue than the airline's existing policy, and that the improved performance of these policies is robust with respect to misspecification errors as well as with respect to errors in parameter estimates.
Author: Jue Wang Publisher: ISBN: Category : Languages : en Pages :
Book Description
[Truncated] Over the last decade, airline markets around the world have been reshaped dramatically by the rapidly growing low-cost carriers and new forms of distribution channel. Significant reduction in searching cost brought by the web-based distribution has made fare product comparison and purchasing an easier task. As a result, traditional demand models based on independent (fare class) demand assumption has been violated. A better understanding of passenger choice behaviour is now needed since the development of new generation revenue management (RM) system requires inputs of demand based on dependent fare classes. Early studies on dependent demand mainly focused on the buy-up and buy-down behaviour for single-leg flights. With the introduction of discrete choice modelling, more recent studies are beginning to incorporate competitions between flights and carriers into the model. In a discrete choice model, a customer is assumed to weigh up service levels of a fare product against its price. The fare option with the highest satisfaction is the one being chosen. As all the components taken into consideration by a traveller may not be readily at hand for the analyst, the satisfaction or utility of a fare product is measured by way of a systematic component - the observed utility - and a random component - the unobserved utility. As such, the choice decision is modelled up to a probability. Discrete choice models are theoretically sound for fare product demand forecasting, as they directly work on the decision making process of air travellers. Currently, the most widely applied discrete choice model in revenue management is the multinomial logit model (MNL), within which the unobserved utility of each alternative is independently and identically distributed (IID). Such a structure leads to the independence from irrelevant alternatives or IIA property. That is, the ratio of probabilities for two alternatives is independent from the existence of any other alternative in the choice set. However, the biggest limitation of IIA is the resulting proportional substitution pattern, which suggests that an improvement in the attributes of one alternative reduces the probabilities for all other alternatives by the same percentage. This highly restricted structure is unlikely to hold in the context of real airline markets. This is because the behaviour of compensatory travellers is likely to vary among the population, and to capture these variations advanced DCMs should be applied.
Author: Curt Cramer Publisher: Springer Nature ISBN: 3658337214 Category : Business & Economics Languages : en Pages : 122
Book Description
The book provides a comprehensive overview of current practices and future directions in airline revenue management. It explains state-of-the-art revenue management approaches and outlines how these will be augmented and enhanced through modern data science and machine learning methods in the future. Several practical examples and applications will make the reader familiar with the relevance of the corresponding ideas and concepts for an airline commercial organization. The book is ideal for both students in the field of airline and tourism management as well as for practitioners and industry experts seeking to refresh their knowledge about current and future revenue management approaches, as well as to get an introductory understanding of data science and machine learning methods. Each chapter closes with a checkpoint, allowing the reader to deepen the understanding of the contents covered.This textbook has been recommended and developed for university courses in Germany, Austria and Switzerland.
Author: Guillermo Gallego Publisher: Springer ISBN: 1493996061 Category : Business & Economics Languages : en Pages : 336
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: Michael C. Clough Publisher: ISBN: Category : Airlines Languages : en Pages : 111
Book Description
Revenue management is at the core of airline operations today; proprietary algorithms and heuristics are used to determine prices and availability of tickets on an almost-continuous basis. While initial developments in revenue management were motivated by industry practice, later developments overcoming fundamental omissions from earlier models show significant improvement, despite their focus on relatively esoteric aspects of the problem, and have limited potential for practical use due to computational requirements. This dissertation attempts to address various modeling and computational issues, introducing realistic choice-based demand revenue management models. In particular, this work introduces two optimization formulations alongside a choice-based demand modeling framework, improving on the methods that choice-based revenue management literature has created to date, by providing sensible models for airline implementation. The first model offers an alternative formulation to the traditional choice-based revenue management problem presented in the literature, and provides substantial gains in expected revenue while limiting the problems computational complexity. Making assumptions on passenger demand, the Choice-based Mixed Integer Program (CMIP) provides a significantly more compact formulation when compared to other choice-based revenue management models, and consistently outperforms previous models. Despite the prevalence of choice-based revenue management models in literature, the assumptions made on purchasing behavior inhibit researchers to create models that properly reflect passenger sensitivities to various ticket attributes, such as price, number of stops, and flexibility options. This dissertation introduces a general framework for airline choice-based demand modeling that takes into account various ticket attributes in addition to price, providing a framework for revenue management models to relate airline companies product design strategies to the practice of revenue management through decisions on ticket availability and price.Finally, this dissertation introduces a mixed integer non-linear programming formulation for airline revenue management that accommodates the possibility of simultaneously setting prices and availabilities on a network. Traditional revenue management models primarily focus on availability, only, forcing secondary models to optimize prices. The Price-dynamic Choice-based Mixed Integer Program (PCMIP) eliminates this two-step process, aligning passenger purchase behavior with revenue management policies, and is shown to outperform previously developed models, providing a new frontier of research in airline revenue management.
Author: Ben Vinod Publisher: Springer Nature ISBN: 3031254562 Category : Business & Economics Languages : en Pages : 182
Book Description
Over the past decade, Artificial Intelligence has proved invaluable in a range of industry verticals such as automotive and assembly, life sciences, retail, oil and gas, and travel. The leading sectors adopting AI rapidly are Financial Services, Automotive and Assembly, High Tech and Telecommunications. Travel has been slow in adoption, but the opportunity for generating incremental value by leveraging AI to augment traditional analytics driven solutions is extremely high. The contributions in this book, originally published as a special issue for the Journal of Revenue and Pricing Management, showcase the breadth and scope of the technological advances that have the potential to transform the travel experience, as well as the individuals who are already putting them into practice.
Author: Cynthia Barnhart Publisher: Springer Science & Business Media ISBN: 1461416086 Category : Business & Economics Languages : en Pages : 461
Book Description
This book reviews Operations Research theory, applications and practice in seven major areas of airline planning and operations. In each area, a team of academic and industry experts provides an overview of the business and technical landscape, a view of current best practices, a summary of open research questions and suggestions for relevant future research. There are several common themes in current airline Operations Research efforts. First is a growing focus on the customer in terms of: 1) what they want; 2) what they are willing to pay for services; and 3) how they are impacted by planning, marketing and operational decisions. Second, as algorithms improve and computing power increases, the scope of modeling applications expands, often re-integrating processes that had been broken into smaller parts in order to solve them in the past. Finally, there is a growing awareness of the uncertainty in many airline planning and operational processes and decisions. Airlines now recognize the need to develop ‘robust’ solutions that effectively cover many possible outcomes, not just the best case, “blue sky” scenario. Individual chapters cover: Customer Modeling methodologies, including current and emerging applications. Airline Planning and Schedule Development, with a look at many remaining open research questions. Revenue Management, including a view of current business and technical landscapes, as well as suggested areas for future research. Airline Distribution -- a comprehensive overview of this newly emerging area. Crew Management Information Systems, including a review of recent algorithmic advances, as well as the development of information systems that facilitate the integration of crew management modeling with airline planning and operations. Airline Operations, with consideration of recent advances and successes in solving the airline operations problem. Air Traffic Flow Management, including the modeling environment and opportunities for both Air Traffic Flow Management and the airlines.
Author: Kalyan T. Talluri Publisher: Springer Science & Business Media ISBN: 0387273913 Category : Business & Economics Languages : en Pages : 731
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: Vasileios S. Zeimpekis Publisher: Springer Science & Business Media ISBN: 0387717226 Category : Business & Economics Languages : en Pages : 249
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.
Author: J. Dai
Author: J. Dai
Author: Jue Wang
Author: Curt Cramer
Author: Guillermo Gallego
Author: Michael C. Clough
Author: Chang Liu
Author: Ben Vinod
Author: Cynthia Barnhart
Author: Kalyan T. Talluri
Author: Vasileios S. Zeimpekis