Statistical Modeling Approach to Airline Revenue Management with Overbooking

Statistical Modeling Approach to Airline Revenue Management with Overbooking

Author: Sheela Siddappa

Publisher:

Published: 2006

Total Pages:

ISBN-13: 9780542722769

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Revenue Management (RM) in the airline industry plays a very important role in maximizing revenue under various uncertainty issues, like customer demand, the number of seats to be maintained in inventory, the number of seats to be overbooked, etc. In this dissertation, a Markov decision process (MDP) based approach using statistical modeling is presented. Prior versions of this statistical modeling approach have employed remaining seat capacity ranges from zero to the capacity of the aircraft. In reality, actual remaining capacities are near capacity when the booking process begins and near zero when the flights depart. Thus, our modified version uses realistic ranges to enable a more accurate statistical model, leading to a better RM policy. We also consider overbooking, no-shows and cancellations and estimate the optimal number of seats to be overbooked using a hybrid approach that combines Newton's and steepest ascent method. The extended statistical modeling approach in this dissertation consists of three modules: (1) the revised statistical modeling module, (2) the overbooking module, and (3) the availability processor module. The first two modules are conducted off-line to identify optimal overbooking pads and derive a policy for accepting/rejecting customer booking requests. The last module occurs on-line to conduct the actual decisions as the booking requests arrive. To enable a computationally-tractable solution method, the revised statistical modeling module, under an assumed maximum overbooking pad of 20%, consists of three components: (1) simulation of the deterministic bid price approach to identify the realistic ranges of remaining seat capacity at different points in time; (2) solutions to deterministic and stochastic linear programming problems that provide upper and lower bounds, respectively, on the MDP value function; and (3) estimation of the upper and lower bound value functions using statistical modeling. Next, the overbooking module identifies the optimal number of seats to be overbooked. Finally, the value function approximations are used with the optimal overbooking pads to determine the RM accept/reject policy.


Airline Revenue Management

Airline Revenue Management

Author: Curt Cramer

Publisher: Springer Nature

Published: 2021-11-10

Total Pages: 122

ISBN-13: 3658337214

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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.


Airline Passenger Cancellations

Airline Passenger Cancellations

Author: Oren Petraru

Publisher:

Published: 2016

Total Pages: 125

ISBN-13:

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Passenger demand forecasting, and subsequently passenger cancellation forecasting, are important components in any airline revenue management (RM) system. Passenger cancellations can potentially lead to flights leaving with empty seats and thus to loss of revenues. Airlines need accurate cancellation forecasting tools in order to properly compensate for cancellations, or in other words, overbook flights above their physical capacity. At the same time, airlines need to be cautious not to overbook too aggressively. If a flight is still overbooked at time of departure, not all passengers are able to board and those left behind need to be compensated and re-accommodated. This thesis focuses on modelling and forecasting passenger cancellations using the PODS booking simulation tool. Several methods for cancellation forecasting and overbooking are presented and their impacts are tested under different demand, competition and RM strategy settings. All methods are based on time series modeling of historical observations. However, the methods differ in terms of the data they use and the canceled bookings they compensate for. The potential contribution of Passenger Name Record data (PNR) to more accurate cancellation forecasting is discussed as well. Simulation results indicate that the ticket revenue gains due to cancellation forecasting and overbooking range between 1.15% and 4.16%, depending on the cancellation forecasting method used and the level of overbooking aggressiveness. However, aggressive overbooking increases the negative effect on revenues due to the costs associated with denied hoardings. Therefore, after taking into account these costs, the net revenue gains range between 0.06% and 2.79%. For airlines with high cancellation rates, the magnitude of the gains from cancellation forecasting and overbooking is even greater, reaching 3.59% in net revenue improvements.


The Theory and Practice of Revenue Management

The Theory and Practice of Revenue Management

Author: Kalyan T. Talluri

Publisher: Springer Science & Business Media

Published: 2006-02-21

Total Pages: 731

ISBN-13: 0387273913

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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.


Improved Forecast Accuracy in Airline Revenue Management by Unconstraining Demand Estimates from Censored Data

Improved Forecast Accuracy in Airline Revenue Management by Unconstraining Demand Estimates from Censored Data

Author: Richard H. Zeni

Publisher: Universal-Publishers

Published: 2001

Total Pages: 274

ISBN-13: 1581121415

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Accurate forecasts are crucial to a revenue management system. Poor estimates of demand lead to inadequate inventory controls and sub-optimal revenue performance. Forecasting for airline revenue management systems is inherently difficult. Competitive actions, seasonal factors, the economic environment, and constant fare changes are a few of the hurdles that must be overcome. In addition, the fact that most of the historical demand data is censored further complicates the problem. This dissertation examines the challenge of forecasting for an airline revenue management system in the presence of censored demand data. This dissertation analyzed the improvement in forecast accuracy that results from estimating demand by unconstraining the censored data. Little research has been done on unconstraining censored data for revenue management systems. Airlines tend to either ignore the problem or use very simple ad hoc methods to deal with it. A literature review explores the current methods for unconstraining censored data. Also, practices borrowed from areas outside of revenue management are adapted to this application. For example, the Expectation-Maximization (EM) and other imputation methods were investigated. These methods are evaluated and tested using simulation and actual airline data. An extension to the EM algorithm that results in a 41% improvement in forecast accuracy is presented.


Modeling Applications in the Airline Industry

Modeling Applications in the Airline Industry

Author: Ahmed Abdelghany

Publisher: Routledge

Published: 2016-04-15

Total Pages: 308

ISBN-13: 1317094891

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Modeling Applications in the Airline Industry explains the different functions and tactics performed by airlines during their planning and operation phases. Each function receives a full explanation of the challenges it brings and a solution methodology is presented, supported by numerical illustrative examples wherever possible. The book also highlights the main limitations of current practice and provides a brief description of future work related to each function. The authors have filtered the rich literature of airline management to include only the research that has actually been adopted by the airlines, giving a genuinely accurate representation of real airline management and its continuing development of solution methodologies. The book consists of 20 chapters divided into 4 sections: - Demand Modeling and Forecasting - Scheduling of Resources - Revenue Management - Irregular Operations Management. The book will be a valuable source or a handbook for individuals seeking a career in airline management. Written by experts with significant working experience within the industry, it offers readers insights to the real practice of operations modelling. In particular the book makes accessible the complexities of the key airline functions and explains the interrelation between them.


Optimal Pricing and Seat Allocation in the Airline Industry Under the Market Competition

Optimal Pricing and Seat Allocation in the Airline Industry Under the Market Competition

Author: Syed Asif Raza

Publisher:

Published: 2007

Total Pages: 0

ISBN-13:

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The current practice of revenue management is either quantity based or price based. A quantity based revenue management is most commonly observed in the airline industry; whereas a price based revenue management is practiced in retail enterprises. Recent improvement of information technology has not only increased the market size, but also has increased market competition. In a competitive environment customers choose among substitutable products depending on several rationalities, however a paramount factor in most selections is price. This thesis investigates pricing issue in revenue management and makes three contributions. First, price based revenue management is studied in the airline industry in a competitive market. Airlines compete for customers using their fare pricing strategies while having fixed capacity allocated in each fare class. The demand for each fare class of an airline is dependent on its fare price and the fare price offered by rival airline(s). A game theoretic approach is used to address the problem assuming both the deterministic and stochastic price sensitive customer demand for each fare class. The existence and uniqueness of Nash equilibrium for the game is shown for both deterministic and stochastic demands. A sensitivity analysis is carried out to determine fare pricing in each fare class considering various situations in the case of deterministic demand. The analysis is further extended to stochastic price sensitive demand, and a sensitivity analysis of the fare prices for each fare class is also reported. Second, an integrated approach to price and quantity based revenue management with an application to the airline industry is presented. The models proposed enable joint control of fare pricing and seat allocation in a duopoly competitive market. Both non cooperative and cooperative bargaining games are studied. Numerical experimentation is performed to study both competitive and cooperative fare pricing along with seat inventory control assuming a nested control on booking limits. In the case of a non cooperative game, Nash equilibrium for the competing airlines is determined assuming both symmetric and asymmetric market competition. A sensitivity analysis based on a statistical design of experiments is also presented to study the behavior of the game. Statistical evidence is established which shows that cooperation improves the revenue to the competing airlines. Lastly, a distribution free approach for pricing in revenue management is explored. The approach assumes the worst possible demand distribution and optimizes the lower bound estimate on revenue, while jointly controlling the price and capacity. The approach is first addressed to revenue management's most commonly observed standard newsvendor problem. Extensions to the problem are identified which can be applied to airline industry. Later the analysis is extended to consider the following cases: a shortage cost penalty; a holding and shortage cost; a recourse cost, with a second purchasing opportunity; and the case of random yields. An application of the approach is also suggested to capacity constrained industries facing restrictions such as limited budget. A numerical study reveals that the approach results in a near optimal estimate on revenue. Using a statistical comparison it is also shown that the outcomes of the standard newsvendor problem are significantly different than its extensions.


A Simulation-based Approach to an Airline Revenue Management Problem

A Simulation-based Approach to an Airline Revenue Management Problem

Author: Emrah Ozkaya

Publisher:

Published: 2002

Total Pages: 90

ISBN-13:

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This thesis studies a well-known problem in the airline industry, namely, the seat-allocation problem in a single leg of an airline flight. The problem studied considers random arrivals, cancellations, no-shows, multiple fare classes, and overbooking. A simulation-based approach using simultaneous perturbation and simulated annealing was used to solve the problem. The performance of these techniques was benchmarked with a widely used heuristic, namely, expected marginal seat revenue heuristic. The results obtained show that the simulation-based optimization approach can outperform the heuristic approach.