The Forty-year History of Revenue Management: Bibliometric Analysis

This paper presents research trends, leading publishers, influential articles, and shifting concerns in the field of Revenue Management for over forty years based on bibliometric analysis. Bibliometric data was retrieved from Web of Science core collection with a well-defined strategy. The data was processed using Network Analysis Interface for Literature Studies Project scripts. Subject-wise and year-wise research trends were presented. The shifting concerns in RM in terms of topic, method, and domain were highlighted using keyword analysis. In general, RM showed an increasing number of published papers with exponential manner every year. The research core in RM covered the three major decisions in RM including pricing, quantity control, and structural decision. It was highlighted that RM’s concern has shifted from single-firm decision to be more consumer- and competition-centric. The data showed that the needs of empirical study and more advanced quantitative methods for complex and real-time problems were urged. In addition, the adoption of RM was extended for industries with semi-flexible capacity. The top influential publishers were Decision Sciences, Operations Research, Management Science, and Management Science Manufacturing & Service Operations Management

A Choice-Based Dynamic Programming Approach for Setting Opaque Prices

Opaque pricing is a form of pricing where certain characteristics of the product or service are hidden from the consumer until after purchase. In essence, opaque selling transforms a differentiated good into a commodity. Opaque pricing has become popular in service pricing as it allows firms to sell their differentiated product at higher prices to regular brand loyal customers while simultaneously selling to non-brand loyal customers at discounted prices. We use a nested logit model in combination with logistic regression and dynamic programming to illustrate how a service firm can optimally set prices on an opaque sales channel. The choice model allows the characterization of consumer trade-offs when purchasing opaque products while the dynamic programming approach allows the characterization of the optimal pricing policy as a function of inventory and time remaining. We compare optimal prices and expected revenues when dynamic pricing is restricted to daily price changes. We provide an illustrative example using data from an opaque selling mechanism (Hotwire.com) and a Washington DC-based hotel

A Data-driven Approach to Revenue Management Problem with Behavioral Considerations

RÉSUMÉ: Cette thèse porte sur l’étude de diverses méthodes avancées et orientées données pour résoudre la problématique de gestion de revenu plus souvent désigné par “Revenue Management” (RM). Nous nous intéressons à deux sous-problèmes du RM que sont la prévision de demande et le contrôle d’inventaire, poursuivons avec une revue de la littérature suivi d’une synthèse des contributions de la thèse. Nous commençons par une introduction générale sur la métodologie usuelle pour traiter la prédiction de la demande et les politiques optimales de contrôle d’inventaire. Nous présentons dans les trois chapitres suivants, nos travaux sur ces problématiques, chacun correspondant à un article soumis dans une revue internationale. Finalement nous concluons par des remarques sur le travail actuel et une discussion sur les possibles futurs travaux. Nous présentons maintenant brièvement les trois articles. Dans le premier article, nous nous intéressons à la prévision de la demande pour une importante compagnie ferroviaire. Pour cela, nous explorons diverses approches de prétraitement, apprentissage machine et sélection de caractéristiques des données. Comme cette prévision est utilisée pour différents objectifs, nous travaillons sur deux niveaux d’agrégation différents. La solution devant être industrialisable, nous mettons l’emphase sur la rapidité, la simplicité et la robustesse. Nous combinons alors des méthodes de l’état de l’art avec des techniques innovantes de construction des caractéristiques des données pour arriver à des résultats prometteurs. Bien que nous traitons la prévision de demande pour le domaine ferroviaire, nos résultats s’appliquent également aux autres domaines de transport et à l’hôtellerie. Dans le second article, nous considérons le problème du contrôle d’inventaire du RM sous comportement d’achat pour le domaine aérien avec une méthode d’apprentissage par renforcement du type “Deep Q-Network” (DQN). Par rapport aux approches traditionnelles en RM, DQN ne dépend pas d’une prévision de la demande pour retourner de bonnes décisions de contrôle. Il fonctionne en utilisant des données historiques et/ou une interaction directe avec les clients. Nous nous concentrons essentiellement sur l’aspect comportemental de notre modèle. Nous entraînons et évaluons notre solution avec des données synthétiques puis la comparons avec des méthodes tradionelles de RM sur des instances aériennes fournies par la littérature. Dans le troisième article, nous abordons des instances de taille plus importante pour des problèmes de RM que l’on retrouve en pratique. Nous proposons un algorithme “Action Generation” (AGen) à intégrer au DQN pour étendre son utilisation à des problèmes de plus grande taille de RM sans trop augmenter le coût de calcul. La motivation derrière cette approche vient d’une analyse des offres optimales à travers l’horizon de réservation qui montre qu’elles sont souvent les mêmes, nous les appelons alors “offres efficaces”. À partir de cette information nous pouvons considérablement réduire le temps de calcul dans les cas pratiques. AGen est un algorithme heuristique de type glouton qui mimique la génération de colonnes dans le but de générer ces “offres efficaces”. La combinaison de DQN et AGen donne des résultats prometteurs sur les problèmes de plus grandes tailles.---------ABSTRATC: This dissertation presents a systematic study of various data-driven advanced methodologies employed to solve a Revenue Management (RM) problem. We address two main modules within an RM system; namely, demand forecasting and inventory control. We start with a general introduction into the thesis and then proceed to overall methodology used to both predict customer demand and analyze the capacity control policies. The methodologies are explained in detail in the three following chapters each of which corresponds to an article already submitted to an international journal. Finally, we conclude with final remarks and discussions of implications for further work. Following is a brief explanation of each article. In the first article, we study a demand forecasting problem to be addressed for a major railway company. To do so, we explore various preprocessing, machine learning and feature engineering techniques. Moreover, the demand is estimated in two different aggregation levels of data in order to serve different purposes. To comply with the industry-specific requirements, the emphasis of our solution method is on speed, simplicity, and robustness. In this study, the use of state-of-the-art machine learning methods along with innovative feature construction techniques led to high quality results. Although railway industry is the representative of our problem, the studied demand forecasting approaches can easily be extended to other transportation industries or hospitality businesses. In the second article, we address a choice-based seat inventory control problem in airline industry using a deep reinforcement learning method named Deep Q-Network (DQN). In contrast to traditional RM techniques, DQN does not rely on predicted demand to make informed capacity control decisions. It operates using historical data and/or real-time interaction with customers. In this study, we mainly focus on the choice-based characteristic of our model. We train and evaluate our solution method with synthetic data and compare the final performance to those of well-known RM methods using common flight examples provided in the literature. In the third article, we tackle large-scale practical RM problems. We propose an “Action Generation” (AGen) algorithm to be integrated into DQN and extend its application to larger RM problems without incurring enormous computational costs. The analysis of the optimal offersets offered to customers throughout the booking horizon shows that only particular offersets (i.e., actions), which we call them “effective sets”, are repeatedly used. Thus, if we manage to develop a method to generate such actions, we will be able to substantially reduce the processing time in practical cases. AGen is a greedy heuristic algorithm that mimics the column generation algorithm [1] with the aim of generating “effective sets”. The AGen embedded DQN yields promising results in large-size network problems

Dynamic Airline Pricing and Seat Availability: Evidence from Airline Markets

Airfares fluctuate due to demand shocks and intertemporal variation in willingness to pay. I estimate a model of dynamic airline pricing accounting for both sources of price adjustments using flight-level data. I use the model estimates to evaluate the welfare effects of dynamic airline pricing. Relative to uniform pricing, dynamic pricing benefits early-arriving, leisure consumers at the expense of late-arriving, business travelers. Although dynamic pricing ensures seat availability for business travelers, these consumers are then charged higher prices. When aggregated over markets, welfare is higher under dynamic pricing than under uniform pricing. The direction of the welfare effect at the market level depends on whether dynamic price adjustments are mainly driven by demand shocks or by changes in the overall demand elasticity

Dynamic Airline Pricing and Seat Availability

Airfares fluctuate over time due to both demand shocks and intertemporal variation in willingness to pay. I develop and estimate a model of dynamic airline pricing accounting for both forces with new flight-level data. With the model estimates, I disentangle key interactions between the arrival pattern of consumer types and scarcity of remaining capacity due to stochastic demand. I show that dynamic airline pricing expands output by lowering fares charged to early-arriving, price-sensitive customers. It also ensures seats for late-arriving travelers with the highest willingness to pay (e.g. business travelers) who are then charged high prices. I find that dynamic airline pricing increases total welfare relative to a more restrictive pricing regime. Finally, I show that abstracting from stochastic demand results in incorrect inferences regarding the extent to which airlines utilize intertemporal price discrimination

Advanced demand and a critical analysis of revenue management

Pre-print; author's draftThis paper presents a theoretical framework of advanced demand through six propositions. The framework introduces the concept of acquisition and valuation risks and suggests that advanced demand distribution is rooted in the trade off between them. Furthermore, since advanced buyers may not consume, firms may be able to re-sell capacity relinquished. The study then proposes how refunds could provide additional revenue to firms. The study further suggests theoretical reasons why and when service firms are able to practice revenue management, suggesting that RM tools such as overbooking and demand forecasting may not be the only tools for higher revenue

Agent Based Modeling of Air Carrier Behavior for Evaluation of Technology Equipage and Adoption

As part of ongoing research, the National Aeronautics and Space Administration (NASA) and LMI developed a research framework to assist policymakers in identifying impacts on the U.S. air transportation system (ATS) of potential policies and technology related to the implementation of the Next Generation Air Transportation System (NextGen). This framework, called the Air Transportation System Evolutionary Simulation (ATS-EVOS), integrates multiple models into a single process flow to best simulate responses by U.S. commercial airlines and other ATS stakeholders to NextGen-related policies, and in turn, how those responses impact the ATS. Development of this framework required NASA and LMI to create an agent-based model of airline and passenger behavior. This Airline Evolutionary Simulation (AIRLINE-EVOS) models airline decisions about tactical airfare and schedule adjustments, and strategic decisions related to fleet assignments, market prices, and equipage. AIRLINE-EVOS models its own heterogeneous population of passenger agents that interact with airlines; this interaction allows the model to simulate the cycle of action-reaction as airlines compete with each other and engage passengers. We validated a baseline configuration of AIRLINE-EVOS against Airline Origin and Destination Survey (DB1B) data and subject matter expert opinion, and we verified the ATS-EVOS framework and agent behavior logic through scenario-based experiments. These experiments demonstrated AIRLINE-EVOS's capabilities in responding to an input price shock in fuel prices, and to equipage challenges in a series of analyses based on potential incentive policies for best equipped best served, optimal-wind routing, and traffic management initiative exemption concepts.