Video games represent the largest and fastest-growing segment of the entertainment industry. Despite its popularity in practice, it has received limited attention from the operations community. Managing product monetization and engagement presents unique challenges due to the characteristics of gaming platforms, where players and the gaming platform have repeated (and endogenously controlled) interactions. These practices have also led to new customer concerns and thus regulation challenges. In this thesis, we describe a body of work that provides the first analytical results for revenue management and matchmaking problems in video games, as well as the fairness issues in many e-commerce platforms. In the first part, we discuss a prevailing selling mechanism in online gaming known as a loot box. A loot box can be viewed as a random bundle of virtual items, whose contents are not revealed until after purchase. We consider how to optimally price and design loot boxes from the perspective of a revenue-maximizing video game company, and provide insights on customer surplus and protection under such selling strategies. In the second part, we consider how to manage player engagement in a game where players are repeatedly matched to compete against one another. Players have different skill levels which affect the outcomes of matches, and the win-loss record influences their willingness to remain engaged. Leveraging optimization and real data, we provide insights on how engagement may increase with optimal matchmaking policies and adding AI bots. In the third part, we consider an increasingly important concern in many e-commerce platforms: the inequality induced by price discrimination. While the practice of discriminatory pricing is generally widespread, it can result in disparate impact against protected groups. We consider the problem of setting prices for different groups of customers under fairness regulations, which limit the differences of various metrics (such as price and demand) across the groups. We show that different types of fairness constraints may not coexist in general, and the impact of fairness levels on social welfare could be non-monotonic and non-trivial.

In this dissertation, I study two topics in the context of revenue management. The First topic involves building a mathematical model to analyze the competition between many retailers who can change the price of their respective products in real time. I develop a game-theoretic model for the dynamic price competition where each retailer's objective is to maximize its own expected total revenue. I use the Nash equilibrium to predict market equilibrium and provide managerial insights into how each retailer should take into account its competitors' behavior when setting the price. The second topic involves working with Amtrak, the national railroad passenger corporation, to develop a revenue management model. The revenue management department of Amtrak provides the sales data of Auto Train, a service of Amtrak that allows passengers to bring their vehicles on the train. I analyze the demand structure from sales data and build a mathematical model to describe the sales process for Auto Train. I further develop an algorithm to calculate the optimal pricing strategy that yields the maximum revenue. Because of the distinctive service provided by Auto Train, my findings make important contribution to the revenue management literature. / Ph. D.

This thesis presents a dynamic pricing model where a seller offers two types of a generic product to a random number of customers. Customers show up sequentially. When a customer arrives, he will ---depending on the prices---either purchase one unit of type 1 product or one unit of type 2 product, or will leave empty-handed. The sale ends either when the entire stock is sold out, or when the customers are exhausted. The seller's task is to post the optimal prices for the two product types to each customer to maximize the expected total revenue. We use dynamic programming to formulate this problem, and derive the optimal policy for special cases. For general cases, we develop an algorithm to approximate the optimal policy and use numerical examples to demonstrate the efficiency of the algorithm. Finally, we apply the results to a continuous-time model where customers arrive according to a Poisson process. We develop a heuristic policy and use numerical examples to show the heuristic policy is very effective. / Master of Science

We consider a continuous time dynamic pricing model where a seller needs to sell a single item over a finite time horizon. Customers arrive in accordance with a Poisson process. Upon arrival, a customer either purchases the item if the posted price is lower than his/her reservation price, or leaves empty-handed. After purchasing the item, some customers, however, will return the item to the seller at an exponential rate for a full refund. We assume that a returned item is in mint condition and the seller can resell it to future customers. The objective of the seller is to dynamically adjust the price in order to maximize the expected total revenue when the sale horizon ends. We formulate the dynamic pricing problem as a dynamic programming model and derive the structural properties of the optimal policy and the optimal value function. For cases in which the customer's reservation price is exponentially distributed, we derive the optimal policy in a closed form. For general reservation price distribution, we consider an approximation of the original model by discretizing both time and the allowable price set. We then present an algorithm for numerically computing the optimal policy in this discrete time model. Numerical examples show that if the discrete price set is carefully chosen, the expected total revenue is nearly the same as that when the allowable price set is continuous. / Master of Science

Bien que le transport de marchandises joue un rôle essentiel dans le secteur économique et que la demande de transport de marchandises augmente, les transporteurs sur le marché du fret ont encore du mal à maintenir et à améliorer leurs revenus. Pour répondre aux défis, Revenue Management (RM) et l’Internet Physique (PI) sont adoptés comme solution dans cette thèse. RM est une méthode, issue de l’industrie du transport aérien, qui permet de maximiser les revenus. PI est un système logistique entièrement interconnecté, ouvert et dynamique visant à développer des réseaux logistiques mondiaux interconnectés ouverts afin d'accroître l'efficacité et la durabilité de la logistique. Cette thèse examine l’application de RM dans PI pour améliorer les revenus des transporteurs de chargement partiel. L’application de RM dans PI est étudiée à partir de quatre questions de recherche sur RM : la tarification, le contrôle de capacité, les prévisions et la tarification groupée. De plus, pour chaque question de recherche, une étude expérimentale est menée pour évaluer la faisabilité et les performances des modèles d'optimisation proposés correspondant à chaque question. Les résultats fournissent aux transporteurs des implications en termes de gestion et des conseils constructifs leur permettant d’optimiser leurs revenus à plusieurs niveaux, en tenant compte de situations et de scénarios différents. Dans l’ensemble, cette recherche examine Revenue Management du point des transporteurs de chargement partiel opérant dans un environnement très dynamique tel que l’Internet Physique. Les travaux de cette recherche donnent un aperçu général et systématique de l’application de Revenue Management dans un réseau dynamique de transport de marchandises par route. Les réalisations de cette thèse fournissent une base pour la future étude approfondie sur le problème des revenus dans un environnement dynamique. / Although the freight transport plays vital role in the economic sector and the freight transport demand is increasing, there are still challenges for the carriers in the freight market to keep and improve their revenue. To respond to the challenges, Revenue Management (RM) and Physical Internet (PI) are adopted as the solution in this thesis. RM is a method, which is originated from airline industry, to maximize the revenue. PI is a fully interconnected, open, dynamic logistics system aiming to develop an open global interconnected logistics networks to increase the logistics efficiency and sustainability. This thesis investigates the application of RM in PI to improve the revenue of less-than-truckload (LTL) carriers. The application of RM in PI is studied based on four research questions in RM, i.e. pricing, capacity control, forecasting, and bundle pricing. In addition, for each research question, an experimental study is conducted to evaluate the feasibility and performance of the proposed optimization models corresponded to each question. The results provide the carriers managerial implications and constructive guidance to make decisions to optimize their revenue at several levels, considering different situations and scenarios. Overall, this research investigates the Revenue Management from the point of view of LTL carriers operating in a highly dynamic environment like Physical Internet. The work in this research gives a general and systematical sight to the application of RM in a dynamic network of road freight transport. The achievements of this thesis give a basis for the future in-depth study on the revenue problem in a dynamic environment.

The progress in information and communication technologies, together with scientific pro-gress in economics, statistics and operational research, influenced the rapid development of process understanding, and estimation and prediction of customer demand, in real-time revenue management. The aim of this work was the detailed description of the essential principles to be included in the business strategy in order to maximize revenue. Network revenue management models seek to maximize revenue when customers buy products with multiple sources. This concept was applied to the example of the aviation industry and a computing program was developed which could be generalized to other industries. This used a "hub-and-spoke" network and programs MS Excel, LINGO and VBA. The paper also describes the basic steps of revenue management that are repeated todetail the fore-casts and dynamically perform re-optimization of the required decisions, to specify the complete process in detail.

[pt] Como conseqüência da desregulamentação da indústria aérea americana nos anos 70, Yield Management YM foi criado como uma ferramenta gerencial para maximizar os lucros e manter as vantagens competitivas de empresas do setor. Com o tempo, essa ferramenta passou a ser utilizada por diversas empresas prestadoras de serviço, quando existe uma demanda variável para uma capacidade fixa e elevado custo de ociosidade, ajudando os gerentes a maximizar as receitas das suas operações. Basicamente, YM é o processo de alocação do tipo certo de capacidade, para cada tipo de cliente, ao preço certo, para que se maximizem os lucros. Pode-se ainda dizer que YM é uma forma sistemática de realizar um tipo de preço discriminatório em função de dados de demanda, de ocupação e dos custos marginais de utilização do recurso (avião, hotel, ou outro sistema prestador de serviço). Esta dissertação tem como objetivo verificar como os maiores hotéis turísticos do Rio de Janeiro se utilizam desta ferramenta em suas operações de reservas e vendas. Para atingir tal objetivo, selecionou-se uma amostra de onze hotéis na orla marítima. Entrevistas foram realizadas com os respectivos gerentes responsáveis pelo processo de reservas e/ou vendas, que responderam a um sobre a atual aplicação dos elementos de YM dentro da organização. Os resultados indicaram que o uso de YM é ainda muito pouco difundido entre esses hotéis, sendo reconhecido e de aplicação incipiente apenas em hotéis de maior porte pertencentes a cadeias, sobretudo internacionais. Alguns impedimentos indicados pelos respondentes e inferidos de suas respostas são comentados. / [en] As a consequence of the deregulation of the American airline industry in the 70 s, Yield Management was created as a managerial tool in order to maximize the profits and to keep the competitive advantages of companies of the sector. With time, this tool was adopted by other service companies, where a flexible demand for a fixed capacity and high under utilization costs exist, helping the managers to maximize their operations revenues. Yield Management (YM), or Revenue Management, is the allocation process of the right type of capacity to each type of customer at the proper price to maximize the sales revenues of services, or of highly perishable goods. It can still be said that YM is a systematic form to carry out a type of discriminatory price to meet demand taking into account occupation data and the marginal cost of resource utilization (airplane, hotel, or another service rendering system). The study presented in this thesis aimed at verifying how the main tourist hotels of Rio de Janeiro are taking advantages of this tool within their reservation and sales processes. To accomplish this objective, a sample of eleven hotels was selected amongst the fifteen more important hotels of the main touristic area of Rio de Janeiro City. Interviews were carried out with the managers responsible for the reservation and, or sales, departament, who answered a questionnaire about the current application of YM elements within the organization. The results have indicated that YM is still very little spread out among these hotels, being recognized and of incipient application, only in large hotel chains, mainly, the international ones. Some obstacles pointed out by the respondents and inferred from their responses are commented.

<p><strong>Syfte</strong>: Studiens syfte är att analysera hur flygbolaget Ryanair prissätter sina flygbiljetter och om det skiljer sig beroende på flygsträcka.</p><p><strong>Metod</strong>: En kvalitativ longitudinell studie med deduktiv ansats där primärdata i form av prisuppgifter samlats in från Ryanairs hemsida.</p><p><strong>Teorier</strong>: Prisdiskriminering, Priselasticitetshävstång, Operationell hävstång och Hallbergs prisstrategier.</p><p><strong>Slutsats</strong>: Studiens resultat bekräftar Anjos, Chengs och Curries teori om att lågprisflygbiljetter håller sig på en ganska stabil nivå fram till att det återstår ungefär 20 dagar kvar till avresa, då priset stiger fram till avgångsdagen då det är som högst. Detta motsäger Anderson och Wilsons teori om att biljettpriset går i cykler och att flygbiljetterna är som dyrast några dagar före avresa för att sedan sjunka i pris. Den höga prishöjningen på avgångsdagen sker konsekvent på alla de undersökta flygsträckorna och är intressant eftersom höga priser inte är något som lågprisflygbolagen vill förknippas med. Förklaringen kan vara det som diskuteras av Piga och Bachis om att flygbolagen försöker skapa en osäkerhet hos resenärer kring när det är billigast att köpa sina flygbiljetter. Studien resultat visar också på att det går att se ett mönster i prissättningen av flygbiljetter på Ryanairs resmål med få flygningar medan de mer populära resmålen har en mer dynamisk prissättning som är desto svårare att förutspå.</p> / <p>The purpose of this Bachelor thesis is to analyze how the airline Ryanair is pricing their tickets and if there is a distinction between different flight destinations. The thesis used a deductive approach and the data (ticket prices) was collected during a time period of 53 days from Ryanair’s internet homepage. The theories used to analyze the data were price discrimination, price elasticity leverage, operating leverage and four different price strategies presented by Niklas L. Hallberg. The result shows that Ryanair uses second degree of price discrimination in all of the observed flights at the departure day. As for the rest, the result shows that there is a distinction between how Ryanair uses pricing as a strategy depending on what the destination is. The conclusion of the thesis is that Ryanair uses different price strategies depending on how popular the specific flight or destination is. The more popular the flight or destination is, the more dynamic the price is and the harder it is to predict the coming price. The pricing of the more popular flights could be, as presented by Piga and Bachis, a way of creating an uncertainty when it is the cheapest to buy airline tickets. Further the Study confirms Anjos, Cheng and Currie’s theory that the prices low-fare tickets are at a stable level until it remains about 20 days until departure, when the price increases until the departure date. This contradicts Anderson and Wilson’s theory that the ticket price goes in cycles where the price is at the highest level some days before departure before it starts to decrease.</p>

Syfte: Studiens syfte är att analysera hur flygbolaget Ryanair prissätter sina flygbiljetter och om det skiljer sig beroende på flygsträcka. Metod: En kvalitativ longitudinell studie med deduktiv ansats där primärdata i form av prisuppgifter samlats in från Ryanairs hemsida. Teorier: Prisdiskriminering, Priselasticitetshävstång, Operationell hävstång och Hallbergs prisstrategier. Slutsats: Studiens resultat bekräftar Anjos, Chengs och Curries teori om att lågprisflygbiljetter håller sig på en ganska stabil nivå fram till att det återstår ungefär 20 dagar kvar till avresa, då priset stiger fram till avgångsdagen då det är som högst. Detta motsäger Anderson och Wilsons teori om att biljettpriset går i cykler och att flygbiljetterna är som dyrast några dagar före avresa för att sedan sjunka i pris. Den höga prishöjningen på avgångsdagen sker konsekvent på alla de undersökta flygsträckorna och är intressant eftersom höga priser inte är något som lågprisflygbolagen vill förknippas med. Förklaringen kan vara det som diskuteras av Piga och Bachis om att flygbolagen försöker skapa en osäkerhet hos resenärer kring när det är billigast att köpa sina flygbiljetter. Studien resultat visar också på att det går att se ett mönster i prissättningen av flygbiljetter på Ryanairs resmål med få flygningar medan de mer populära resmålen har en mer dynamisk prissättning som är desto svårare att förutspå. / The purpose of this Bachelor thesis is to analyze how the airline Ryanair is pricing their tickets and if there is a distinction between different flight destinations. The thesis used a deductive approach and the data (ticket prices) was collected during a time period of 53 days from Ryanair’s internet homepage. The theories used to analyze the data were price discrimination, price elasticity leverage, operating leverage and four different price strategies presented by Niklas L. Hallberg. The result shows that Ryanair uses second degree of price discrimination in all of the observed flights at the departure day. As for the rest, the result shows that there is a distinction between how Ryanair uses pricing as a strategy depending on what the destination is. The conclusion of the thesis is that Ryanair uses different price strategies depending on how popular the specific flight or destination is. The more popular the flight or destination is, the more dynamic the price is and the harder it is to predict the coming price. The pricing of the more popular flights could be, as presented by Piga and Bachis, a way of creating an uncertainty when it is the cheapest to buy airline tickets. Further the Study confirms Anjos, Cheng and Currie’s theory that the prices low-fare tickets are at a stable level until it remains about 20 days until departure, when the price increases until the departure date. This contradicts Anderson and Wilson’s theory that the ticket price goes in cycles where the price is at the highest level some days before departure before it starts to decrease.

The traditional air cargo supply chain is composed by the shippers, the freight forwarders and the airlines. The freight forwarders secure capacity with airlines in order to accommodate shippers' demand. They bid for capacity six to twelve months before the actual departure date of the aircraft, and confirm the needed capacity a few days before departure. We address the freight forwarders' problem of confirming needed capacity based on balancing the costs of ordering too much capacity versus ordering too little. We use a Markov decision process to model the problem. We show the value function is convex in the state variables for lead times of one and two periods. We present the structure of the optimal policy and show it is stationary. In addition we present solutions to the case with subcontracting options and order due dates. We also address the airlines' revenue management problem with respect to its cargo capacity available for free sale (after honoring committed capacity to freight forwarders), in particular the problems of (1) accepting/rejecting incoming bookings based on bid prices, and of (2) estimating the show-up rate (ratio of bookings handed in at departure over bookings on hand) with impact on overbooking. To address the lumpiness of demand, we split the cargo into two categories: small cargo, composed of mail and small packages, and large cargo, composed of the bulk of commercial cargo. The small cargo is approximated with the passenger arrival, and we propose a new algorithm to solve the traditional probabilistic nonlinear problem from the passenger side. The large cargo is solved using a dynamic program, which is decomposed at the leg level using a fare-prorating scheme. The solution from our new approach is shown via simulation to be superior to two approaches currently used: the first come first serve, and the deterministic linear program. The show-up rate is estimated using wavelets and we show that a discrete show-up rate is more suitable than the traditional Normal estimator used in practice. The new estimator results in considerable more potential revenue.