Optimizing Airline Ticket Purchase Timing

Our approach in this paper is to suggest the user to either buy or wait for the purchase of airline tickets.Airline tickets prices are volatile and keep on varying depending on various parameters. Users, not having much information about these parameters, are often forced to buy tickets at high prices. This paper proposes a machine learning based prediction system which uses logistic regression to suggest users to buy the ticket, implying that prices are going to rise in coming days or wait for some time implying prices are going to plummet in coming days. This system also predicts the price of the date user wants to travel

Sample Efficient Policy Search for Optimal Stopping Domains

Optimal stopping problems consider the question of deciding when to stop an observation-generating process in order to maximize a return. We examine the problem of simultaneously learning and planning in such domains, when data is collected directly from the environment. We propose GFSE, a simple and flexible model-free policy search method that reuses data for sample efficiency by leveraging problem structure. We bound the sample complexity of our approach to guarantee uniform convergence of policy value estimates, tightening existing PAC bounds to achieve logarithmic dependence on horizon length for our setting. We also examine the benefit of our method against prevalent model-based and model-free approaches on 3 domains taken from diverse fields.Comment: To appear in IJCAI-201

Forecasting flight prices with machine learning models : a comparative analysis between low and high-cost airlines

Forecasting fight prices is a challenging task due to the complex nature of the pricing algorithms that airlines use. Apart from the fact that these algorithms are not public, they have to take into account many different variables that affect ticket prices. Since the airlines’ demand forecasting may not always hold true as a result of varying demand, prices need to be adjusted accordingly. This approach is called dynamic pricing. It is a technique of price discrimination based on temporal differences mainly, leading to the widely spread assumption that the time of booking is a crucial determinant of the ticket price. This analysis shows that apart from days to departure, especially fight distance and airline type infuence the price significantly. That is, longer fights as well as fights operated by full-service carriers, as opposed to low-cost carriers, are usually more expensive. This thesis uses a dataset including the fight fares and other fight-related characteristics of one-way fights in the US between April and October 2022, retrieved from the search engine Expedia.com. The data is used to train and compare the performance of several supervised learning models aiming to forecast fight prices. Each model is deployed three times, first with the entire dataset, and then once with data only from low-cost-carrier and only from full-service-carriers, respectively. The most accurate models for all three datasets are the random forests followed by k-nearest-neighbor. The results of this thesis suggest that a large part of the fight price can be predicted using fight-related details such as days to departure and fight duration, yet, it also shows that there remains a certain inexplicable variability that could be due to external factors that are not included in the present analysis.Prever os preços de voo é uma tarefa desafiante devido à natureza complexa dos algoritmos de fixação de preços que as companhias aéreas utilizam habitualmente. Para além da sua natureza privada, estes algoritmos levam em consideração muitas variáveis diferentes que afetam, por essa via, os preços das passagens aéreas. Uma vez que a previsão da procura pelas rotas das companhias aéreas nem sempre se mantém válida devido à sua variabilidade ao longo do tempo, os preços precisam de ser ajustados continuamente de modo a favorecer a rentabilidade dessas companhias. Esta prática designa-se por fixação de preços dinâmica, uma técnica de discriminação de preços baseada principalmente em diferenças temporais, levando à amplamente difundida perceção de que o momento da reserva é o principal determinante do preço das passagem aéreas. A presente análise revela que, para além do número de dias até à data de partida, o tipo de companhia aérea e, sobretudo, a distância de voo também influenciam significativamente o respetivo preço. Assim, voos mais longos e operados por companhias de serviço completo, em oposição às companhias de baixo custo, são geralmente mais caros. A presente tese utilizou uma base de dados incluindo os preços das passagens aéreas e outras características relacionadas com voos de ida nos EUA entre abril e outubro de 2022, obtidas através do motor de busca Expedia.com. Estes dados foram utilizados para treinar e comparar o desempenho de vários modelos de aprendizagem automática supervisionada com o objetivo de prever os preços de voo. Cada modelo foi implementado três vezes, primeiro com a base de dados completa, depois com os registos relativos às companhias de baixo custo e, finalmente, apenas com os dados das companhias de serviço completo. Os modelos mais precisos para os três conjuntos de dados são as florestas aleatória seguidos pelos modelos de K vizinhanças próximas. Os resultados deste trabalho sugerem que uma parte significativa do preço pode ser prevista utilizando detalhes relacionados com o voo, como o número de dias até a partida e a duração da viagem. Contudo, permanece uma certa variabilidade não explicada que pode dever-se a fatores externos não incluídos na presente análise

Total cost of ownership purchasing of a service : the case of airline slection at Alcatel Bell.

The multiple objective problem of purchasing for business falls into two broad categories: the purchasing of components for manufacturing and the purchasing of services. Several supplier selection models have been suggested in the literature for the purchasing of production-related components. To our knowledge, no supplier selection model for the purchasing of services has been published. In this paper we elaborate on a mathematical programming model that selects suppliers of a multiple item service and simultaneously determines market shares of the suppliers selected. The methodology is based on the collection of Total Cost of Ownership (TCO) information, quantifying all the costs associated with the purchasing process throughout the entire value chain of the firm. We apply this methodology to the real life case study of selecting airlines for 56 destinations at Alcatel Bell and have obtained TCO savings of 19.5%.Purchasing; Selection; Manufacturing; Models; Mathematical programming; Suppliers;

An Optimal Airline Revenue Management Seat Pricing Plan Model

The goal of an airline is to sell tickets at the highest fare possible, thus yielding maximum profit for the stakeholders. As airline seat pricing is divided into different fare classes, a revenue management system is created and maintained to identify opportunity costs where the airline may sell an optimum number of available seats in both discounted fare and full fare classes. Ideally, under perfect conditions, the airline will sell all available seats at full capacity for each leg of a trip. Under non-ideal conditions for the airline, not all available seats may sell at either full fare or discounted fare prices, thus resulting in potential revenue loses. This study will present an optimal model of an airline revenue management seat pricing plan to maximize revenue for each leg of a trip. The recommended discounted fare and full fare seats in the economy class will be calculated under a desired optimal full capacity seating plan

Demand Management Opportunities in E-fulfillment: What Internet Retailers Can Learn from Revenue Management

In this paper, we explain how Internet retailers can learn from proven revenue management concepts and use them to reduce costs and enhance service. We focus on attended deliveries as these provide the greatest opportunities and challenges. The key driver is service differentiation. Revenue management has shown that companies can do much better than a one-size-fits-all first-come-first-serve strategy when selling scarce capacity to a heterogeneous market. Internet retailers have strong levers at their disposal for actively steering demand, notably the offered delivery time windows and their associated prices. Unlike traditional revenue management, these demand management decisions affect both revenues and costs. This calls for a closer coordination of marketing and operations than current common practice.ketenbeheer;revenue management;home delivery;E-fulfillment;demand management;marketing-operations interface

Discrete Choice Models for Revenue Management

In the transportation field, the shift of airline and railway industries toward web-based distribution channels has provided passengers with better access to fare information. This has resulted in passengers becoming more strategic to price. Therefore, a better understanding of passenger choice behavior is required in order to support fare strategies. Methods based on discrete choice (DC) analysis have recently been introduced in revenue management (RM). However, applications of DC models in railway ticket pricing are limited and heterogeneity in choice behavior across different categories of travelers has mostly been ignored. Differences in individual taste are crucial for the RM sector. Additionally, strategic passenger behavior is significant, especially in markets with flexible refund and exchange policy, where ticket cancellation and exchange behavior has been recognized as having major impacts on revenues. This dissertation examines innovative approaches in discrete choice modeling to support RM systems for intercity passenger railway. The analysis, based on ticket reservation data, contributes to the existing literature in three main aspects. Firstly, this dissertation develops choice models of ticket purchase timing which account for heterogeneity across different categories of passengers. The methodology based on latent class (LC) and mixed logit (ML) model framework offers an alternative approach to demand segmentation without using trip purposes which are not available in the data set used for the analysis. Secondly, this dissertation develops RM optimization models which use parameters estimated from the choice models and demand functions as key inputs to represent passenger response to RM policy. The approach distinguishes between leisure and business travelers, depending on departure time and day of week. The formulated optimization problem maximizes ticket revenue by simultaneously solving for ticket pricing and seat allocation. Strategies are subjected to capacity constraints determined on the basis of the railway network characteristics. Finally, this dissertation develops ticket cancellation and exchange model using dynamic discrete choice model (DDCM) framework. The estimated model predicts the timing of ticket cancellations and exchanges in response to trip schedule uncertainty, fare, and refund/exchange policy of the railway service. The model is able to predict new departure times of the exchanged tickets and covers the full range of departure time alternatives offered by the railway company

Will they take this offer? A machine learning price elasticity model for predicting upselling acceptance of premium airline seating

Employing customer information from one of the world's largest airline companies, we develop a price elasticity model (PREM) using machine learning to identify customers likely to purchase an upgrade offer from economy to premium class and predict a customer's acceptable price range. A simulation of 64.3 million flight bookings and 14.1 million email offers over three years mirroring actual data indicates that PREM implementation results in approximately 1.12 million (7.94%) fewer non-relevant customer email messages, a predicted increase of 72,200 (37.2%) offers accepted, and an estimated $72.2 million (37.2%) of increased revenue. Our results illustrate the potential of automated pricing information and targeting marketing messages for upselling acceptance. We also identified three customer segments: (1) Never Upgrades are those who never take the upgrade offer, (2) Upgrade Lovers are those who generally upgrade, and (3) Upgrade Lover Lookalikes have no historical record but fit the profile of those that tend to upgrade. We discuss the implications for airline companies and related travel and tourism industries.© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Competition in the advanced sale of service capacity

Pre-print of an article accepted for publication in International Journal of Revenue Management; authors' draft dated March 6, 2008; final version available at http://www.inderscience.com