Developing a good strategy is important in today’s competitive and commoditized lodging market. A good strategy necessitates knowledge of what strategic actions can increase firms’ profits and maintain their profitability throughout market cycles. However, less research effort has been made to date to find and operationalize strategic actions of hotels that lead to higher performance. This dissertation empirically examines both short-and long-run strategic decisions of hotels and their consequences at the micro level, the result of which can be used to develop a good strategy ensuring sustainable success in business. The first part of this dissertation investigates the effect of conformity and differentiation on performance and performance risk of hotels, focusing on their long-run strategic tools—location, capacity, and quality. The second part of this dissertation examines the efficacy of hotels’ room rate discounts in performance recovery after a crisis, in which the price is a hotels’ short-run strategic tool. Using standard econometric methods and applicable variations, this dissertation found empirical evidence supporting hypotheses in both parts of the dissertation. / Tourism and Sport

In this thesis, we study three revenue management problems where we propose simple algorithms with provable guarantees. While online marketplaces provide retailers with tremendous flexibility, they are often large, noisy, have multiple stakeholders, and could be more challenging to characterize. These complexities give rise to a preference for simple, interpretable policies. Further, traditional marketplaces such as brick-and-mortar stores cannot always leverage tools designed for online environments due to physical constraints, higher latency, etc. With these motivations in mind, we develop algorithms for assortment optimization and pricing that are easy to implement in practice and have theoretical justifications for their performance. In Chapter 1, we consider a dynamic assortment optimization problem where the seller has a fixed inventory of multiple substitutable products to sell over a fixed time horizon. We consider two modifications to the traditional problem. First, we simplify the assortment planning by restricting assortment changes to "product retirements". When a product is retired, it becomes unavailable to all future customers. Second, we assume the seller has flexibility regarding which customers to approach. In each period, the seller chooses which subset of products to retire and selects a customer to visit. The selected customer then receives an option to purchase one of the available products, i.e., non-retired products with positive remaining inventory. We provide two policies for this problem. Our first policy guarantees a constant fraction of the best possible revenue. Our second policy is near-optimal but requires the problem to have a specific structure. In Chapter 2, we study the fundamental joint pricing and inventory management problem. The optimal policy for the model we consider is known to be an (s, S, p) policy: when the inventory level drops to s units, the seller immediately places an order to replenish the inventory to S units. Specifically, the optimal pricing policy p has a different price for every inventory state. We proposed simple policies requiring no more than three prices and prove that these policies are near-optimal compared to optimal policies which require more prices and are less robust. In particular, when orders cannot be backlogged, we show that a single price is sufficient for good performance. In Chapter 3, we analyze assortment optimization and pricing with opaque products. An opaque product is one for which only partial information is available to the buyer at the time of purchase. When a customer selects the opaque product, the seller can fulfill the purchase using any of the offered products. Opaque products can help sellers boost total sales. We propose simple policies for assortment optimization with provable constant factor guarantees, which are near-optimal in numerical experiments. We also provide upper bounds for the advantage of selling opaque products.

The number of trips on highways and urban networks has significantly increased in the recent decades in many cities across the world. At the same time, the road network capacities have not kept up with this increase in travel demand. Urban road networks in many countries are severely congested, resulting in increased travel times, increased number of stops, unexpected delays, greater travel costs, inconvenience to drivers and passengers, increased air pollution and noise level, and increased number of traffic accidents. Expanding traffic network capacities by building more roads is extremely costly as well as environmentally damaging. More efficient usage of the existing supply is vital in order to sustain the growing travel demand. Travel Demand Management (TDM) techniques involving various strategies that increase the travel choices to the consumers have been proposed by the researchers, planners, and transportation professionals. TDM helps create a well balanced, less automobile dependent transportation system. In the past, several TDM strategies have been proposed and implemented in several cities around the world. All these TDM strategies, with very few exceptions, are static in nature. For example, in the case of congestion pricing, the toll schedules are previously set and are implemented on a daily basis. The amount of toll does not vary dynamically, with time of day and level of traffic on the highway (though the peak period tolls are different from the off-peak tolls, they are still static in the sense that the tolls don't vary continuously with time and level of traffic). The advent of Electronic Payment Systems (EPS), a branch of the Intelligent Transportation Systems (ITS), has made it possible for the planners and researchers to conceive of dynamic TDM strategies. Recently, few congestion pricing projects are beginning to adopt dynamic tolls that vary continuously with the time of day based on the level of traffic (e.g. I-15 value pricing in California). Dynamic TDM is a relatively new and unexplored topic and the future research attempts to provide answers to the following questions: 1) How to propose and model a Dynamic TDM strategy, 2) What are the advantages of Dynamic TDM strategies as compared to their Static counterparts, 3) What are the benefits and costs of implementing such strategies, 4) What are the travel impacts of implementing Dynamic TDM strategies, and 5) How equitable are the Dynamic TDM strategies as compared to their Static counterparts. This dissertation attempts to address question 1 in detail and deal with the remaining questions to the extent possible, as questions 2, 3, 4, and 5, can be best answered only after some real life implementation of the proposed Dynamic TDM strategies. Two novel Dynamic TDM strategies are proposed and modeled in this dissertation -- a) Dynamic Congestion Pricing and b) Dynamic Highway Space Inventory Control System. In the first part, dynamic congestion pricing, a real-time road pricing system in the case of a two-link parallel network is proposed and modeled. The system that is based on a combination of Dynamic Programming and Neural Networks makes "on-line" decisions about road toll values. In the first phase of the proposed model, the best road toll sequences during certain time period are calculated off-line for many different patterns of vehicle arrivals. These toll sequences are computed using Dynamic Programming approach. In the second phase, learning from vehicle arrival patterns and the corresponding optimal toll sequences, neural network is trained. The results obtained during on-line tests are close to the best solution obtained off-line assuming that the arrival pattern is known. Highway Space Inventory Control System (HSICS), a relatively new demand management concept, is proposed and modeled in the second half of this dissertation. The basic idea of HSICS is that all road users have to make reservations in advance to enter the highway. The system allows highway operators to make real-time decisions whether to accept or reject travellers' requests to use the highway system in order to achieve certain system-wide objectives. The proposed HSICS model consists of two modules -- Highway Allocation System (HAS) and the Highway Reservation System (HRS). The HAS is an off-line module and determines the maximum number of trips from each user class (categorized based on time of departure, vehicle type, vehicle occupancy, and trip distance) to be accepted by the system given a pre-defined demand. It develops the optimal highway allocations for different traffic scenarios. The "traffic scenarios-optimal allocations" data obtained in this way enables the development of HRS. The HRS module operates in the on-line mode to determine whether a request to make a trip between certain origin-destination pair in certain time interval is accepted or rejected. / Ph. D.

"Uncertainty" is used broadly to refer to things that are unknown or incompletely understood. In operations management, basic sources of uncertainty may include decision uncertainty, model uncertainty, analytical uncertainty, data uncertainty, and so on. Although uncertainty is unavoidable in decision making, different mechanisms can be designed to mitigate the impact of uncertainty. One commonly used strategy is "decision postponement," wherein the decision maker purposefully delays some of the decisions to a time when uncertainty is reduced or resolved. This type of a recourse action provides the decision maker with increased ability to match supply with demand. In this dissertation, we study the value of decision postponement in the context of different settings, including capacity investment, revenue management, and supply chain coordination. These problems share one characteristic in common: decision postponement, and as such, are all modeled as two-stage stochastic programming problems. In the first stage, a set of decisions are made under uncertainty so as to maximize the expected profit or utility. Then in the second stage, all uncertainty is resolved and a deterministic optimization problem is solved to determine the postponed decisions, constrained by the first stage decisions. In capacity investment, we study the capacity, pricing, and production decisions of a monopolist producing substitutable products with flexible or dedicated resources. While the capacity decision needs to be made ex-ante, under demand uncertainty, pricing and production decisions can be postponed until after uncertainty is resolved. We show how key demand parameters (the nature of uncertainty, market size, market risk, and risk attitude) impact the optimal capacity decision under the linear demand function. In particular, we show that if the demand shock is multiplicative, then in terms of the "invest or not" decision, the firm will be immune to forecast errors in parameters of the underlying demand shock distribution. Furthermore, incorrectly modeling the demand shock as additive, when, in fact, it is multiplicative, may lead to overinvestment. On the other hand, while the concept of a growth in market size leads to similar conclusions under both additive and multiplicative demand shocks, how market risk affects the optimal capacity decision depends critically on the form of the demand shock. In addition, the decision-maker's attitude toward risk significantly affects the optimal capacity level, and its impact highly depends on the structure of the resource network. Our analysis provides insights and principles on the optimal capacity investment decision under various settings. In airline revenue management, a well-studied problem is the optimal allocation of seat inventory among different fare-classes, given a capacity for the flight and a demand distribution for each class. In practice, capacity on a flight does not have to be fixed; airlines can exercise some flexibility on the supply side by swapping aircraft of different capacities between flights as partial booking information is gathered. This provides the airline with the capability to more effectively match their supply and demand. In this dissertation, we study the seat inventory control problem considering the aircraft swapping option. Our analytical results demonstrate that booking limits considering the swapping option can be considerably different from those under fixed capacity. We also show that principles on the relationship between the optimal booking limits and demand characteristics (size and risk) developed for the fixed-capacity problem no longer hold when swapping is an option. We develop new principles and insights on how demand characteristics affect the optimal seat allocation under the swapping possibility. We also perform a numerical study, which indicates that the revenue impact of using the "true" optimal booking limits under the swapping possibility can be significant. In supply chain coordination, we consider the influenza vaccine supply chain, which, due to the biological complexity of the production process, has a unique characteristic in that production yield is highly uncertain. Given the market demand and price, a monopolist supplier must decide how much raw material to input into production in the first stage. However, since the yield is unknown and production is costly, it is not necessarily in the supplier's best interest to ensure that all market demand is met. The supplier's input quantity depends on the trade-off between the costs of overproduction and undersupply. This, in fact, is one of the reasons why the influenza vaccine manufacturers in the United States lack motivation to produce sufficient amounts of vaccine to meet all demand [Williams (2005), Chick et al. (2008)]. In operations management, it is a well-known result that decentralized supply chains, where each player is only interested in optimizing her own objective, often lead to poor overall performance for the supply chain. However, a higher efficiency is achievable through contracting on a set of transfer payments [Cachon (2004)]. A "coordinating" contract is referred to as one in which each player's objective is in accordance with the supply chain's objective. Given the fact that influenza vaccine plays an important role in health care industry, it is important to study how different contracts impact the influenza vaccine supply chain, where the uncertainty is on the supply side. We study a game in which the supplier and the retailer are engaged in certain type of contracts that specify how risk is shared between the players. We study both the pre-ordering and the post-ordering settings, which respectively refer to the cases where the retailer orders the vaccine before or after the vaccine production is completed. We show that pre-ordering wholesale price contracts dominate post-ordering wholesale price contracts in terms of the resulting supply chain efficiency, but neither of them are able to fully coordinate the supply chain. We also find that cost-sharing contracts are able to coordinate the supply chain, while payback and advance-ordering wholesale price contracts fail to do so. Finally, we prove that if the unsold vaccine can be salvaged with some positive value, then the supply chain can be easily coordinated with wholesale price contracts. In studying this type of stochastic programming problems, it is not only important to characterize the optimal solution, but also important to gain an understanding of how the optimal solution will be affected by environmental parameters. Since the most inaccurate part in stochastic programming often lies in the parameters of the distribution functions, it is both interesting and meaningful to investigate how the optimal solution varies with the intrinsic nature of the random variables. Consequently, we make use of stochastic order relationships to study the behavior of the optimal solutions when the underlying random variables become either "larger" or "more risky." / Ph. D.

The U.S. hardwood industry has traditionally depended on the domestic demand to sustain levels of production above 14 billion board feet per year. Because of the collapse of the U.S. housing market in 2009 and the economic recession that followed, the industry moved its sight to the international markets, as an opportunity to replace some lost demand, and pursue long term growth. Previous research on international marketing of hardwood products indicates that, there is a growing concern among U.S. companies to understand the main competitiveness factors in key markets such as Asia and Europe. Finding opportunities to add value to U.S. hardwood exports has been the goal of this research project. A case study and survey research were carried out among importers and exporters, whereby it was found that aspects related to price, quality and service, are critical in achieving competitive advantage. This motivated a study in demand and pricing management, which found that these tasks may be subject to innovation through optimization approaches. / Ph. D.

Thesis (MBA) -- Stellenbosch University, 2007. / ENGLISH ABSTRACT: The study was motivated by the widely reported problem of poor financial management within South African local government and, more specifically, the deteriorating status of revenue management, especially in medium to smaller municipalities. The reasons for the alleged poor financial management are complex and involve issues of management capacity, inappropriate systems, and socio-economic circumstances. It is obvious that a holistic approach will be required to address such a multi-ciimensional problem. This research proposes to suggest solutions to the revenue management problems facing South African municipalities. If this issue is not resolved urgently, local government will impede service delivery rather than improve it. Service delivery is key to the integration of South Africa's first economy into a second economy. The specific objectives of the research are; firstly, to develop a revenue management value chain model specific for municipalities; and, secondly, to conduct an analysis of the Lukhanji Local Municipality's financial status against the value chain model. To attain the objectives of this study existing literature and the scope of the problem must first be examined and understood. It is evident that the prevailing revenue management problem experienced, lie with the local government themselves in terms of their inadequate financial management. These problems have placed Significant pressure on municipalities' cash flows and eroded their financial resources. In turn, this has resulted in questionable sustainability and financial via bility, as the problematic cash position of municipalities has impacted negatively on their capital and maintenance expenditure. One of the greatest challenges facing local government is the collection of revenue raised for services rendered to various consumers. Of the various actions taken by government to address the current crisis situation in municipalities in the short to medium term, national grants to sponsor capital projects and services have been key in Stellenbosch University http://scholar.sun.ac.za iv ensuring that the decrease in municipal expenditure has not been greater, preventing the potentially disastrous consequences. The Municipal Finance Management Act, together with the Municipal Structures Act, Municipal Systems Act; and also the Constitution provide a well -defined financial framework within which local government can function. The research also reviewed all four main revenue sources of local government, namely own revenue {utility fees and property tax}; subsidies through intergovernmental transfers; loans; and private-sector equity. Despite the improvement in intergovernmental transfers, it is important that all municipalities maximise their own revenue, while considering those that cannot afford basic services. The research proposes the following revenue management value chain model, specifically for South African municipalities: Revenue Planning Indigent Management Tanff Setling ~ Metering Billing Customer Database Management Revenue Coliection I) Credit Control Loss Management In the analYSis of the Lukhanji Local Municipality's financial status against the value chain model, it became evident that problems existed in all elements of the value chain. General financial and viability assessments supported the above finding and indicated financial difficulties and serious cash-flow problems. The research present several recommendations regarding the improvement of poor financial management in South African municipalities and the Lukhanji Local Municipality. However, in brief, urgent attention is required in terms of the Lukhanji Local Municipality's cash-flow in the short term, while a performance-driven culture needs to be developed in the long term. Lastly, local government in general, and the Lukhanji Local Municipality specifically, will succeed in improving their revenue management if they harness the collective will and skills of all the stakeholders involved in the provision of services. / AFRIKAANSE OPSOMMING: Die studie spruit voort uit die negatiewe publisiteit oor die probleem van swak finansi~le bestuur in plaaslike regering en, meer spesifiek, die verslegtende stand van inkomstebestuur, veral in mid del slag- tot klein munisipaliteite. Die redes is ingewikkeld en behels probleme met bestuursvermo~, onvanpaste stelsels, 'n kultuur van nie-hetaling, en sosio-ekonomiese omstandighede. 'n Holistiese benadering is ooglopend nadig om die vee/dimensionele probleem op te los. Die doel van die navorsing is om by te dra tot die vind van oplossings vi r die inkomstebestuursprobleme wat munisipaliteite in die gesig staar. Indien die situasie nie dringend omgekeer word nie, bestaan die gevaar dat ons munisipaliteite in bankrotskap gebring kan word , wat beteken dat hulle nie hul grondwetlike verpligting sal kan nakom nie en dat hulle dienslewering sal belemmer en dit verbeter nie. Goeie dienslewering is die sleutel vir die integrasie van Suid Afrika se eerste ekonomie met die tweede ekonomie. Die doelwitte van die navorsing is; eerstens, om 'n spesifieke inkomstebestuurs-waardekettingmodel vir munisipaliteite te ontwikkel; en meedens, om Lukhanji Plaaslike Munisipaliteit se finansiele status teen die model te meet. Om die doelwitte te bereik moet die probleem vestaan word deur bestaande literatuur te bestudeer . Oit is duidelik dat die heersende inkomstebestuurs probleme h§ by die plaaslike regering in terme van hul onvoldoende finansiele bestuur. Die probleme het geweldige druk op munisipaliteite se kontantvloei gesit en sodoende hul hulpbronne gedreineer. Oit het 'n vraagteken geplaas op die volhoubaarheid en finansiele lewensvatbaarheid van munisipaliteite as gevolg van die kontant probleme se negatiewe impak op die kapitale en instandhouding bestedings. Een van die grootste uitdagings waarvoor plaaslike regering te staan kom, is die invordering van inkomste wat gehef word vir dienste wat aan verskeie verbruikers gelewer word . As dit nie vir die Stellenbosch University http://scholar.sun.ac.za vii nation ale tussenregeringsoordragte, vir beide dienslewering en kapitaal projekte was nie, sou munisipaliteite met baie erger probleme gesit het. Die Wet op die Bestuur van Munisipale Finansies, tesame met die Wet op Munisipale Strukture; die Wet op Munisipale Stelsels; asook die Grondwet verskaf 'n goed omskrewe finansif!le raamwerk binne plaaslike regering. Die studie behandel die vier vernaamste inkomstebronne van plaaslike regering, wat eie inkomste (utiliteitsgelde en eiendomsbelasting), subsidies deur tussenregeringsoordragte, lenings en privaatsektor-ekwiteit insluit. Ten spyte van die verbetering van tussenregeringsoordragte vir sommige jurisdiksies, is dit belangrik dat aile munisipaliteite huJ eie inkomste maksimeer, met die fokus op diegene wat wei basiese dienste kan bekostig. Die navorsing doen die volgende inkomstebestuurs-waardekettingmodel spesifiek vir munisipaliteite aan die hand: Inkomstebeplanning Bestuur van hulpbehoe- Tarief· wendes vasstelling C. Metenesing Fakturering Klante- Databasisbestuur Inkomsteinvordering ~ Kredietbeheer Verliesbestuur By die ontleding van die Lukhanji Plaaslike Munisipaliteit se finansiele status teenoor die waardekettingmodel het dit duidel ik geblyk dat probleme in al die elemente van die waardeketting bestaan. Die algemene finansiele ontleding en finansiele lewensvatbaarheidsmodel ondersteun die bostaande bevindinge en toon dat die Munisipaliteit in ernstige finansiele moeilikheid verkeer. Die navorsing sluit verskeie onderling velWante aanbevelings oor die verbetering van swak finansieJe bestuur in Suid Afrikaanse munisipaliteite en die Lukhanji PlaasJike Munisipaliteit in. Die Munisipaliteit se kontantvloei het veral dringende aandag nodig in die korttermyn, terwyl die Munisipaliteit 'n kultuur van prestasiebestuur in the langtermyn moet ontwikkel. Plaaslike regering oor die algemeen en die Lukhanji Plaaslike Munisipaliteit in die besonder sal net in hul inkomstebestuur slaag as hulle die gesamentlike wil en vermof!ns van al die belanghebbendes wat by die verskaffing van dienste betrokke is, kan aanwend.

Cette thèse développe des algorithmes pour des problèmes de transport ferroviaire et est réalisée en partenariat avec l'entreprise Eurotunnel qui exploite le tunnel sous la Manche. Ce partenariat s'est établi sous la forme d'une chaire avec l'École des Ponts où cette thèse a été menée. Nous développons trois sujets dans cette thèse: le premier est un problème opérationnel rencontré par Eurotunnel, les deux autres sont plus prospectifs et théoriques, et sont inspirés des problèmes de transport ferroviaire d'Eurotunnel.Le processus de création de grilles horaires pour le transport ferroviaire se découpe en plusieurs phases (estimation de la demande, détermination du réseau, planification des départs, affectation des trains et du personnel). Nous nous intéressons dans une première partie à la phase de planification des départs des trains sur un intervalle temporel, appliquée au cas spécifique d'Eurotunnel. L'objectif est de calculer les horaires des départs des trains depuis chacune des deux stations (Coquelles en France et Folkestone en Angleterre) en respectant des contraintes d'exploitation (sécurité, chargement, ...) et des accords commerciaux signés avec leurs partenaires (Eurostar, ...). De plus, la prise en compte des retards dès la planification des départs est primordiale pour limiter la propagation des perturbations de train en train sur le réseau. Nous avons développé des algorithmes de planification pour Eurotunnel tenant compte des contraintes du réseau et de la probabilité de retard pour chaque train. Ces algorithmes utilisent des outils standard de la Recherche Opérationnelle pour modéliser et résoudre ces problèmes d'optimisation.La tarification des billets est un enjeu majeur pour les entreprises de transport. Pour les compagnies aériennes, de nombreux algorithmes ont été étudiés pour définir le prix optimal des billets pour différentes classes de passagers. Nous appliquons dans une deuxième partie des méthodes standard de tarification (modèles de choix discrets) afin d'optimiser de manière globale les prix et les horaires des départs pour des entreprises de transport ferroviaire. Des outils classiques de l'optimisation stochastique, des modèles de choix discrets et des heuristiques sont utilisés dans nos algorithmes pour donner les meilleures solutions possibles en un temps de calcul limité.Nous nous intéressons dans une dernière partie à une classe de problèmes de transport, inspirés de ceux rencontrés par Eurotunnel, en donnant des algorithmes efficaces de résolution exacte ou approchée. Ces algorithmes permettent de donner une borne supérieure de la complexité temporelle de ces problèmes. La classe de problèmes étudiés consiste en la planification des départs de navettes sur une ligne fixe, pour transporter d'une station A vers une station B des usagers arrivant de manière continue. Les navettes sont éventuellement autorisées à faire de multiples rotations pour transporter plusieurs vagues d'usagers. L'objectif est de limiter le temps d'attente des passagers avant le départ de leur navette. Des combinaisons originales de l'optimisation convexe et de la théorie des graphes (problèmes de plus court chemin) sont utilisées dans nos algorithmes / This thesis develops algorithms for rail transportation problems, conducted in relationship with the company Eurotunnel which operates the tunnel under the Channel. This partnership is a scientific chair with the École des Ponts et Chaussées, where this thesis was realized. We study three topics throughout the thesis: the first one is an operational problem faced by Eurotunnel, whereas the two other ones are prospective and theoretical problems inspired by their process.The planning process for rail transportation can be divided into several phases (demand estimation, line planning, scheduling of the departure times, rolling stock and crew planning). In a first part, we focus on the scheduling phase on a time interval, applied to the specific case of Eurotunnel. The objective is to compute the departure times of the trains for each of the two stations (Calais in France and Folkestone in England), satisfying operation constraints (security, loading, ...) and commercial agreements with their partners (Eurostar, ...). Moreover, taking into account the delays in the scheduling phase is essential to limit the propagation of the disturbances from train to train in the network. We develop scheduling algorithms for Eurotunnel taking into account the operation and commercial constraints, and the random distributions of the delays for each train. These algorithms use standard tools of Operations Research to model and solve these optimization problems.Pricing is a main issue for transportation companies. Many algorithms have been proposed to help airline companies to define optimized prices of the plane tickets for different classes of passengers. In a second part, we apply some standard pricing frameworks (discrete choice models) in order to optimize in a global way the prices and the departure times of the trains for rail transportation companies. Standard tools of stochastic optimization, discrete choice models, and some heuristics are used in our algorithms to compute the best possible solutions in a limited computation time.We focus in a last part on a class of transportation problems, inspired form Eurotunnel. We give efficient algorithms to solve exactly or to approximate the optimal solutions of these problems. These algorithms give an upper bound of the time complexity of this class of problems. The problems studied consist in scheduling the departure times of shuttles on a fixed trip, to transport passengers, arriving continuously at an initial station, to a given destination. The shuttles are potentially allowed to perform several rotations to transport several groups of passengers. The objective is to minimize the waiting time of the passengers before the depart of their shuttle. Original combinations of convex optimization and graph theory (shortest path problems) are used in our algorithms

Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Systems Science and Industrial Engineering Department, 2006. / Includes bibliographical references.

In this thesis, the seat inventory control problem is studied for airlines from the perspective of a risk-averse decision maker. There are only a few studies in the revenue management literature that consider the risk factor. Most of the studies aim at finding the optimal seat allocations while maximizing the expected revenue and do not take the variability of the revenue and hence a risk measure into account. This study aims to decrease the variance of the revenue by increasing the capacity utilization called load factor in the revenue management literature. In addition to expected revenue, load factor is an important performance measure the state companies work with. For this purpose, two types of models with load factor formulations are proposed. This thesis is the first study in the revenue management literature for the airline industry that uses the load factor formulations in the mathematical models. It is an advantage to work with load factor formulations since the models with load factor formulations are much easier to formulate and solve as compared to other risk sensitive models in the literature. The results of the proposed models are evaluated by using simulation for a sample network under different scenarios. The models we propose allow us to control the variability of revenue by changing the used capacity of the aircraft. This is at the expense of a decrease in the revenue under some scenarios. The models we propose perform satisfactorily under all scenarios and they are strongly recommended to be used especially for the small-scale airline companies and state companies and for scheduling new flights even in large scale, well established airline companies.

Accurately representing the price-demand relationship is critical for the success of a price optimization system. This research first uses booking data from 28 U.S. hotels to investigate the validity of two key assumptions in hotel revenue management. The assumptions are: 1) customers who book later are willing to pay higher rates than customers who book earlier; and, 2) demand is stronger during the week than on the weekend. Empirical results based on an analysis of booking curves, average paid rates, and occupancy rates for group, restricted retail, unrestricted retail, and negotiated demand segments challenge the validity of these assumptions. The combination of lower utilization rates and greater product differentiation suggests that hotels should apply different approaches than simply matching competitor rates to avoid losing market share. On days when inventory is near capacity, traditional yield management tactics deliver tremendous value, but these should be augmented by incorporating price response of demand and competition effects. On days when demand is soft and occupancy is projected to be low, price and competition based strategies should dominate. The hotel price optimization problem with linear demand model is a quadratic programming problem with prices of products that utilize multiple staynight rooms as the decision variable. The optimal solution of the hotel price optimization problems has unique properties that enables us to develop an alternative optimization algorithm that does not require solving quadratic optimization problem. Using the well known least norm problem as a subroutine, the optimization problem can be solved as finding a minimum distance between a polyhedron defined by non-negative demand and capacity constraints. This algorithm is efficient when only a few of the staynights are highly constrained. In practice, the choice of a demand model is largely driven by the ease of estimation and model fit statistics such as R2 and mean absolute percentage error (MAPE). These metrics provide measures of statistical validity of the model, however, they do not measure how well the price optimization will perform which is the ultimate interest of the practitioners. In order to measure the impact of demand models on price optimization performance, we first investigate the goodness of fit of linear demand models with different driver variables using actual data from 23 U.S. hotels representing multiple brands and location types. We find that hotels within the same location types (such as urban, suburban, airport) share similar driver variables. Airport and suburban hotels have simpler model specifications with less drivers compared to the urban hotels. The airport hotel demand models are different from other location hotels in that the airport hotel demand level does not differ by day of week. We then measure the impact of demand model misrepresentation on the performance of price optimization through simulation experiments, which are performed for different levels of demand and forecast accuracy to represent various market environments that hotels operate in. We find that using models with missing driver variables can reduce the potential revenue by 13%∼53% and using the wrong functional form 5%∼43% under our simulation environment. The findings from our research imply that correctly representing the demand model in price optimization is crucial to its success. In order for hotels to realize the maximum potential revenue through pricing, efforts should be focused on identifying the major driver variables influencing demand including the ones that we found to be significant.