A review of revenue management : recent generalizations and advances in industry applications

Originating from passenger air transport, revenue management has evolved into a general and indispensable methodological framework over the last decades, comprising techniques to manage demand actively and to further improve companies’ profits in many different industries. This article is the second and final part of a paper series surveying the scientific developments and achievements in revenue management over the past 15 years. The first part focused on the general methodological advances regarding choice-based theory and methods of availability control over time. In this second part, we discuss some of the most important generalizations of the standard revenue management setting: product innovations (opaque products and flexible products), upgrading, overbooking, personalization, and risk-aversion. Furthermore, to demonstrate the broad use of revenue management, we survey important industry applications beyond passenger air transportation that have received scientific attention over the years, covering air cargo, hotel, car rental, attended home delivery, and manufacturing. We work out the specific revenue management-related challenges of each industry and portray the key contributions from the literature. We conclude the paper with some directions for future research

Revenue Management Strategies for Long-Term Survival of Small-Farm Wineries

Some owners of small-farm wineries have moved to direct and alternative revenue management strategies to generate revenue and create brand awareness because of increased competition and regulatory changes. Research has revealed that owners of small-farm wineries remain financially reliant on direct-to-consumer sales through tasting rooms that represent an estimated 70% of their total revenue generated. This qualitative multiple case study was an exploration of how revenue management decisions of small-farm winery owners may contribute to long-term survival in a regulated industry. Dynamic capabilities concept was the conceptual framework for this study. The study population consisted of 3 small-farm winery owners in Connecticut who have operated a winery with Connecticut Grown designation for at least 10 years. Data were collected through semistructured interviews, organizational documents, observation notes, and review of each winery\u27s website. Three themes emerged from data analysis: focus on brand and customer base, constraints consideration, and competitors\u27 impact. The findings and recommendations from this study may further small-farm winery owners\u27 understanding of revenue management strategies they can use to overcome constraint challenges and mitigate competitors\u27 impact. As small-farm winery owners improve profitability and sustain long-term survival, subsequent positive social change, such as small business development and increased employment opportunities, may lead to economic prosperity for the local community and financial stability of community residents

Revenue Maximization Using Product Bundling

Product bundling is a business strategy that packages (either physically or logically), prices and sells groups of two or more distinct products or services as a single economic entity. This practice exploits variations in the reservation prices and the valuations of a bundle vis-à-vis its constituents. Bundling is an effective instrument for price discrimination, and presents opportunities for enhancing revenue without increasing resource availability. However, optimal bundling strategies are generally difficult to derive due to constraints on resource availability, product valuation and pricing relationships, the consumer purchase process, and the rapid growth of the number of possible alternatives.This dissertation investigates two different situations—vertically differentiated versus independently valued products—and develops two different approaches for revenue maximization opportunities using product bundling, when resource availability is limited. For the vertically differentiated market with two products, such as the TV market with prime time and non-prime time advertising, we derive optimal policies that dictate how the seller (that is, the broadcaster) can manage their limited advertising time inventories. We find that, unlike other markets, the revenue maximizing strategy may be to offer only the bundle, only the components, or various combinations of the bundle and the components. The optimality of these strategies critically depends on the availability of the two advertising time resources. We also show how the network should focus its programming quality improvement efforts, and investigate how the "value of bundling," defined as the network's and the advertisers' benefit from bundling, changes as the resource availabilities change. We then propose and study a bundling model for the duopolistic situation, and extend the results from the monopolistic to the duopolistic case.For the independently valued products, we develop stochastic mathematical programming models for pricing bundles of n components. Specializing this model for two components in a deterministic setting, we derive closed-form optimal product pricing policies when the demand functions are linear. Using the intuition garnered from these analytical results, we then investigate two procedures for solving large-scale problems: a greedy heuristic, and a decomposition method. We show the effectiveness of both methods through computational experiments

Stochastic Bilevel Models for Revenue Management in the Hotel Industry

RÉSUMÉ : La Gestion du Revenu consiste à maximiser le revenu des compagnies. Cette technique est pratiquée, entre autres, dans les secteurs de l’aéronautique, des télécommunications et de l'hôtellerie. Dans cette thèse, nous développons et résolvons un modèle stochastique biniveau pour l’industrie hôtelière qui est considérée, de nos jours, comme une industrie mûre caractérisée par une forte compétition et une gestion des inventaires compliquée. Nous avons remarqué que durant ces trente dernières années, la recherche dans le domaine de la gestion du revenu dans l’industrie hôtelière n’a pas proposé ou résolu de modèles qui considèrent simultanément l’affectation des inventaires, le prix, la longueur du séjour, la qualité de service et l’incertitude. Par conséquent, le but de cette thèse est de développer un nouveau modèle de gestion du revenu dans l’industrie hôtelière qui permet aux gestionnaires d’hôtels de prendre en compte certaines données pertinentes pour la prise des décisions relatives à la tarification et l’affectation des inventaires en se basant sur une meilleure compréhension du comportement des clients et de l’incertitude du marché. Nous nous inspirons pour cela des modèles biniveau de tarification et des modèles stochastiques à deux étapes. Dans le cas déterministe, le meneur (leader) de l’industrie essaie, au niveau supérieur, de fixer les prix de ses inventaires de façon à maximiser ses revenus. Puis, les usagers essaient, au niveau inférieur, de minimiser leurs dépenses en fonction des différentes alternatives. Dans le but d'introduire le facteur de l'incertitude, nous avons développé un modèle stochastique à deux étapes: à la première étape, le meneur, comme dans le cas déterministe, fixe ses prix en maximisant ses profits. Puis, chaque groupe d’utilisateurs choisit, au niveau inférieur, les inventaires les moins chers tout en considérant les attributs qu'ils ont préalablement définis (distance et qualité de service). À la seconde étape, nous introduisons de l'incertitude sur le prix fixé par les concurrents ainsi que sur la demande. En réaction, le meneur doit ajuster ses prix et ses affectations d’inventaires, ce qui implique des changements dans les distributions des groupes d’usagers aussi. Ces deux étapes sont liées par des contraintes absolues et proportionnelles relatives à la variation du prix de chaque inventaire. Comme ce modèle est un modèle stochastique biniveau à deux étapes, il hérite la propriété NP-Difficile du modèle biniveau déterministe. Dans ce modèle, nous considérons que l’incertitude peut être modélisée en utilisant des vecteurs aléatoires qui suivent une certaine distribution de probabilité connue. Cette information peut provenir des données historiques ou d’une connaissance empirique de la fonction de masse qui représente fidèlement la vraie distribution. Nous supposons que les vecteurs aléatoires ont un nombre fini de réalisations qui, dans notre cas, correspondent aux scénarios. Afin de résoudre notre modèle, nous avons développé non seulement des stratégies exactes, mais aussi des heuristiques. La stratégie exacte consiste à transformer le problème de base en un problème MIP (Mixed Integer Program), qui est standard pour ce type de problème. La principale réussite en termes d’heuristiques est le développement d’une heuristique gloutonne capable de résoudre le problème de manière efficace. Cette heuristique consiste à copier les prix des concurrents et à ré-optimiser en faveur du meneur. Pour continuer avec une recherche globale, le processus d’exploration a été suivi par un problème MIP restreint qui se base sur la solution fournie par notre heuristique. Finalement, la stratégie exacte supportée par les heuristiques consiste à ajouter au problème MIP original une heuristique qui cherche les solutions entières, par la procédure d’évaluation et séparation progressive (B&B), et qui permet d’ajuster directement la borne inférieure. Une fois que les heuristiques et le modèle ont été développés, nous avons créé un processus de génération de données. Ce processus cherche non seulement à générer des instances réalistes pour l’industrie, mais aussi à éviter les situations atypiques. Pour cela, nous avons modélisé la fluctuation du prix et de la demande en utilisant des variables aléatoires uniformes, et nous avons développé un processus analytique qui permet d’ignorer rapidement les situations atypiques. Les résultats numériques sont présentés pour les trois stratégies précédentes. Le résultat le plus satisfaisant est celui basé sur notre heuristique complétée par un problème MIP restreint. De plus, les résultats obtenus sont en accord avec le comportement économique. Selon que le meneur a ou n’a pas d’avantage compétitif en ce qui concerne la localisation des hôtels, il aura un comportement plus ou moins prédateur face à ses concurrents. Dans le cas où il a un avantage compétitif, le meneur cherchera à imiter le prix de ses concurrents afin d’attirer les groupes d’usagers offrant les revenus les plus importants. Lorsque le meneur n’est pas dans une position avantageuse, il fixera ses prix plus bas que ses concurrents pour attirer les groupes d’utilisateurs qui sont sensibles à la distance, mais aussi ceux qui sont plus sensibles à la qualité du service. Pour cela, il devra relocaliser ses inventaires en ignorant les groupes d’usagers qui lui procureront de faibles revenus. Finalement, un certain nombre d’analyses de sensibilité ont été réalisées pour évaluer la performance du modèle. Premièrement, nous avons introduit la stochasticité simultanément sur le prix et la demande. Ensuite, nous avons complexifié le modèle en variant la capacité de l’industrie. Notre heuristique a permis d’obtenir un résultat conforme au comportement économique espéré. Par conséquent, les principales contributions de cette recherche sont: l’élaboration d’un modèle complexe pour la gestion des revenus hôteliers, la résolution de grands et de petits exemples en un temps de calcul raisonnable, l’obtention de bons résultats grâce à l’utilisation de notre heuristique (même si nous ne pouvons pas garantir qu’il s’agit de la solution optimale), et l’offre de résultats utiles pour la prise de décision dans l’industrie hôtelière.----------ABSTRACT : Revenue Management consists in maximizing a company’s revenue. This technique is applied in the airline, telecommunications, and hospitality industry, among others. In this thesis, we develop and solve a stochastic bilevel model for the hotel industry, which is nowadays considered as a mature industry marked by an intense competition and by a complex inventory management. We noticed that over the last 30 years, Hotel Revenue Management research has not proposed and solved models that consider simultaneously inventory assignments, price, length of stay, quality of service and uncertainty. Therefore, the purpose of this doctoral research is to develop a new model for Hotel Revenue Management that is inspired from bilevel pricing models and from the Two-stage Stochastic Models and that allows hotel’s managers to account with useful data for pricing decision and assignment allocation, based on a better understanding of consumers’ behavior and market uncertainty. In a deterministic model, the leader of the industry tries to set prices to its inventories, maximizing its revenue in the upper level, and users choose the lowest cumulative expenditures among available alternatives, at the lower level. In order to introduce uncertainty information, we have developed a two-stage model: in the first stage the leader set its prices with the goal of maximizing profits in the upper level, and each users’ group chooses the least expensive inventory considering the attributes previously defined by them (distance and quality of service), at the lower level. In the second stage, we introduce uncertain information about competitors’ prices and demand, and thus the leader must set again its prices and inventory allocations, which also implies changes in users’ group distributions. The stages are tied by price variation in each inventory through an absolute and proportional constraint. It is difficult to solve the bilevel programming problem. The non-convexity usually present in bilevel programming results in the complexity of the solution algorithm. Even a very simple bilevel problem is still a NP-hard problem The NP-hard property of deteministic bilevel programs is also present in our two-stage stochastic bilevel model. We consider that uncertainty can be modeled with the support of random vectors that follow a known distribution function. This information might come from historical data or from the empirical knowledge of the distribution function, and that is close to the true unknown uncertainty. We assume that the random vectors have a finite number of realizations, which in our case corresponds to the scenarios. In order to solve our model, we developed not only exact strategies but also heuristics. The exact strategy consisted in transforming the basic problem into a MIP problem using the KKT conditions (or optimality conditions), through the use of big constants and auxiliary binary variables. The main achievement in terms of heuristics is the development of our greedy heuristic, which was able to solve the problem efficiently. This heuristic consisted in copying competitors’ prices and re-optimizing in favor of the leader. To keep a global search, the exploration process was followed by a MIP restricted problem that took as origin the solution provided by our heuristic. Finally, the exact strategy supported by heuristics consisted in adding to the MIP original problem a heuristic that looks for integer solutions directly in the branch and bound (B&B) tree. Once the model and the heuristics were developed, a data generation process was designed. The procedure sought not only to generate realistic instances for the industry but also to avoid unfeasible situations. To do this, we modeled price and demand fluctuations through the use of uniform random variables and we developed an analytical process that allowed us to disregard quickly atypical situations. The numerical results are presented for the two previous strategies, being the most performing the one based on our heuristic complemented with the MIP restricted problem. Moreover, the obtained results performed as expected in terms of its economic behavior. Depending on having or not a competitive advantage with respect to the location of its hotels, the leader has a more or less predatory behavior with its competition. In a situation under a competitive advantage, the leader seeks to imitate the price of its competitors in order to attract users’ groups that provide the highest revenue. If the leader is not in an advantageous position, it set lower prices than the competition to compensate users’ groups more sensible to distance. At the same time, it set competitive prices to attract users’ groups that are more sensitive to quality of service than to distance, which implies that the leader reallocates its inventories and disregards users’ groups providing lower revenues. Finally, a certain number of sensitivity analyzes were conducted to evaluate the performance of the model. First, we introduced stochasticity on price and demand simultaneously and then, we added more complexity by varying the capacity of the industry. The heuristic was able to obtain a result, which was again behaving economically as expected. Therefore, the main contributions of this research are to provide a elaborated model for Hotel Revenu Management, to solve small and large instances in a reasonable computing time, to obtain good results through the use of our heuristic (although we cannot assure it is the optimal solution), and to provide very useful results such as: pricing information, users group distribution in inventories, users group revenue contributions, sensitivity to capacity parameters, for decision making in the hotel industry

A Stochastic Dynamic Programming Approach to Revenue Management in a Make-to-Stock Production System

In this paper, we consider a make-to-stock production system with known exogenous replenishments and multiple customer classes. The objective is to maximize profit over the planning horizon by deciding whether to accept or reject a given order, in anticipation of more profitable future orders. What distinguishes this setup from classical airline revenue management problems is the explicit consideration of past and future replenishments and the integration of inventory holding and backlogging costs. If stock is on-hand, orders can be fulfilled immediately, backlogged or rejected. In shortage situations, orders can be either rejected or backlogged to be fulfilled from future arriving supply. The described decision problem occurs in many practical settings, notably in make-to-stock production systems, in which production planning is performed on a mid-term level, based on aggregated demand forecasts. In the short term, acceptance decisions about incoming orders are then made according to stock on-hand and scheduled production quantities. We model this problem as a stochastic dynamic program and characterize its optimal policy. It turns out that the optimal fulfillment policy has a relatively simple structure and is easy to implement. We evaluate this policy numerically and find that it systematically outperforms common current fulfillment policies, such as first-come-first-served and deterministic optimization

Evaluando el progreso de la eficiencia con tecnología en una cadena de hoteles española

This paper analyzes the changes in the total factor productivity index of a Spanish hotel chain in the period from 2007 to 2010 with the purpose of identifying efficiency patterns for the chain in a period of financial crisis. The data envelopment analysis (DEA) Malmquist productivity index was used to estimate productivity change in 38 hotels of the AC chain. Results reveal AC hotels’ efficiency trends and, therefore, their competitiveness in the recession period; they also show the changes experienced in these hotels’ total productivity and its components: technological and efficiency changes. Positive efficiency changes were due to positive technical efficiency rather than technological efficiency. The recession period certainly influenced the performance of AC Hotels, which focused on organizational changes rather than investing in technology.Este artigo analisa as mudanças no fator total de produtividade de uma cadeia de hotéis na Espanha, no período de 2007-2010, com o propósito de identificar os padrões da cadeia em um período de crise financeira. O índice data envelopment analysis (DEA) Malmquist de produtividade foi usado para estimar a mudança da produtividade nos 38 hotéis da AC Cadeia de Hotéis. Os resultados revelaram as tendências de eficiência e competitividade da AC Hotéis em um período de recessão, bem como as mudanças vivenciadas na produtividade total e, consequentemente, em seus componentes de eficiência e tecnológicos. O período de recessão influenciou, sem dúvida, o comportamento da AC Hotéis, que buscou mais mudanças organizacionais do que tecnológicas.Este artículo analiza los cambios del índice de productividad del factor total de una cadena de hoteles españoles en el periodo de 2007 hasta 2010, con el propósito de identificar patrones de eficiencia para la cadena en un periodo de crisis financiera. El índice de productividad data envelopment analysis (DEA) Malmquist fue utilizado para estimar el cambio de productividad en 38 hoteles de la cadena AC. Los resultados revelan las tendencias de la eficiencia de los hoteles AC y, por lo tanto, su competitividad en el periodo de recisión; ellos también demuestran los cambios experimentados en la productividad total de eses hoteles y sus componentes: cambios de eficiencia y tecnológicos. Cambios de eficiencia positivos se debieron más bien a eficiencias técnicas positivas que a eficiencias tecnológicas. El periodo de recesión ciertamente ha influenciado los Hoteles AC, que enfocaron más en los cambios organizacionales que en invirtiendo en tecnología