Dissertations / Theses on the topic 'Airline network management'

The airline industry is facing unprecedented challenges in generating sufficient revenues to stay in business. Airlines must capture the greatest revenue yield from every flight by leaving no seats unsold and not over filling the cabin with discount fares. To succeed in doing the above airlines must be able to accurately forecast each of their market segments, manage product andprice availability to maximize revenue and react quickly to competitive changes in the market place. Thus seat inventory control and ticket pricing form the two major tools of revenue management. The focus of this paper is to consolidate the ideas of seats inventory control and pricing in order to maximize the revenues generated by an airline network. A continuous time yield management model for a network with multiple legs, multiple fare classes and dynamic price changes for all fare classes is considered. Each fare class has a set of fares from which the optimal fare is chosen based upon the Minimum Acceptable Fare (MAF) which performs the critical role in the decision process. A machine Learning based algorithm, EMSRa based and EMSRb based algorithm for obtaining dynamic policies for combined pricing and allocation. The algorithms are implemented for a sample network with eight cities, eleven logs, thirty origin-destinations(ODs), three fare classes, three levels of fares in each class and ninety itineraries.

La gestion des revenus d’un réseau de compagnies aériennes, un des problèmes le plus critiques dans le secteur du transport aérien, a reçu une attention significative depuis ces dernière décennies. Cependant, de nombreuses problématiques doivent encore être traitées. Cette thèse étudie quatre nouveaux problèmes de la gestion des revenus dans un réseau de compagnies aériennes. D'abord, un problème de dimensionnement de capacité du réseau avec alliances concurrentes est étudié. Dans ce problème, les concurrences horizontales et verticales sont considérées et la demande est supposée déterministe. L’objectif est de maximiser les revenus globaux de l’alliance en déterminant la capacité (en nombre de places) dans les vols pour chaque classe tarifaire de chaque compagnie. Le problème est formulé en programmation linéaire en nombres entiers et résolu à l’aide du solveur CPLEX. Deuxièmement, un problème intégrant la localisation de p-hub médian et le dimensionnement des capacités (places) est étudié pour maximiser une combinaison du bénéfice moyen et du bénéfice au pire cas. Pour ce problème, un seul hub à capacité illimitée est considéré. De plus, les incertitudes sur la demande sont représentées à l’aide d’un ensemble fini des scénarios. Le problème est formulé en programmation stochastique à deux étapes. Ensuite, un algorithme génétique (GA) est proposé pour résoudre le problème pour chaque scénario. Les résultats numériques montrent que la méthode est meilleure que celles dans la littérature qui considèrent uniquement le bénéfice moyen. Le troisième problème étudié est une extension naturelle du deuxième dans lequel la capacité de hub à localiser est limitée et les perturbations qui peuvent impacter la capacité du hub, telles que des conditions météorologiques, sont prises en compte. Deux formulations du problème sont proposées : (1) une programmation stochastique à deux étapes sur la base des scénarios, et (2) optimisation hybride de programmation stochastique à deux étapes à l’aide de pondération. Ensuite, l’approximation moyenne par échantillonnage (SAA) et le GA sont appliqués pour résoudre le problème, respectivement. Les résultats numériques montrent que la SAA est plus performante que le GA. Le quatrième problème est aussi une extension du deuxième problème où la compagnie aérienne doit respecter le niveau d'émissions de CO2 imposé. Le problème est modélisé en programmation stochastique à deux étapes sur la base des scénarios. De plus, une méthode SAA est proposée pour sa résolution<br>As one of critical problems in aviation industry, airline network revenue management has received significant attention in recent decades. However, many issues still need to be addressed. This thesis investigates four new airline network revenue management problems. Firstly, a network capacity allocation problem with competitive alliances is studied. In this problem, horizontal and vertical competitions and deterministic demand are considered. The aim is to maximize the global alliance revenue by determining the (seat) capacities in flights for each fare class of each airline. The problem is formulated into a mixed integer programming and is solved by a commercial solver CPLEX. Secondly, an integrated p-hub median location and (seat) capacity allocation problem is investigated to maximize the combined average-case and worst-case profits of an airline. For this problem, an uncapacitated hub is considered and uncertain demand is represented by a finite set of scenarios. The studied problem is formulated based on a two-stage stochastic programming framework. Then a Genetic Algorithm (GA) is proposed to solve the problem for each scenario. Computational results show that the proposed method outperforms those in the literature only considering average-case profit. The third studied problem is a generalization of the second one in which the capacity of hub to be located is limited and disruptions which can impact airline hub capacity, such as adverse weather, are considered. Two formulations of the problem are proposed based on : (1) a scenario-based two-stage stochastic programming, and (2) a weight-based hybrid two-stage stochastic programming-robust optimization framework. Then a Sample Average Approximation (SAA) method and a GA are applied to solve them, respectively. Computational results show that the SAA is more effective than the GA. The fourth problem is also an extension of the second one where an airline is subjected to a CO2 emission limit. The problem is modeled into a scenario-based two-stage stochastic programming. And a SAA method is proposed to solve it

Thesis (S.M. in Management Studies)--Massachusetts Institute of Technology, Sloan School of Management, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 114-116).<br>In response to the many challenges faced by US airlines in the past decade, merger activity has increased significantly. By combining their networks, airlines commonly aim to not only realize cost synergies but also achieve revenue synergies as well through increased network coverage. In practical terms, this means that the combined airline can cut its total capacity without reducing traffic as it benefits from a larger network and more connecting options via its hubs. The objective of this thesis is to find evidence to confirm this effect based on recent merger activity by comparing both capacity and traffic data before and after the integration period. Particular emphasis is placed on the changing role of hubs to highlight capacity and traffic shifts in a combined network. Two of the most recent major mergers, Delta-Northwest and United-Continental, exhibit how the networks of previously independent carriers were consolidated to achieve the above-mentioned synergies. Delta concentrated capacity at its largest hub in Atlanta and a small number of additional hubs while other hubs experienced a significant downsizing. Additionally, the airline also eliminated a large number of point-to-point services that were bypassing the hubs in order to maximize the use of its hubs. United and Continental, on the other hand, engaged in fairly minor capacity redistribution instead of sweeping reductions. Both carriers increased the share of capacity operated by regional partners and grew capacity between most of the hubs as well. Over the same time frame, however, both of the combined airlines lost passengers compared to their pre-merger levels. While exogenous factors like the recent recession and operational issues played a role, network strategies at both airlines also affected traffic. Delta was unable to recover most of the passengers it lost on the eliminated point-to-point services. For United, the shift towards more international capacity indicates a displacement of domestic traffic by international connecting passengers. Although both carriers had not returned to their pre-merger traffic levels by the end of the integration period, Delta's 2012 performance suggests that network integration and consolidation can have positive effects in the long run.<br>by Henning Greiser.<br>S.M.in Management Studies

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2004.<br>Includes bibliographical references (p. 263-269).<br>(cont.) the essential factors affecting traditional measures of airline performance following low-fare entry. Our simulation results show that these measures are greatly affected by the entrant's capacity relative to the incumbent, by the incumbent carrier's competitive pricing response, and by the competitive revenue management situation. For example, average fares on the incumbent carrier can either increase or decrease following entry by a new competitor, depending on whether one or both airlines perform revenue management. In an extension of the simulations to a larger network environment, it is also shown that network flows of passengers affect the performance of all competitors, as measured by aggregate measures of performance. Furthermore, use of advanced network revenue management allows the incumbent carriers to rely on connecting passengers to mitigate the effect of entry on network revenues, but leads to amplified effects at the local market level. Consequently, this research establishes that traditional aggregate measures of airline performance on their own do not constitute a reliable indication of the response of incumbent carriers, and provide even less information on their strategic intent, which is critical in identifying predation. This research also demonstrates the relationships between aggregate measures of performance and previously overlooked factors including relative entrant capacity, competitive pricing and revenue management, and flows of network passengers.<br>The recent growth of low-fare, low-cost carriers has changed the competitive airline environment. In the US alone, low-fare carrier market shares have increased from just over 5% in 1990 to almost 25% in 2003. Traditional network carriers have consequently had to adjust to the changing competitive environment, which has led to cost reductions, fare structure simplifications and service adjustments. In addition, competitive responses of incumbent network carriers to low-fare entry have prompted concern regarding the potential for predatory practices in the airline industry. Assessment of unfair competitive practices in airline markets has typically been based on the analysis of changes in aggregate measures, such as average fares, traffic and revenues. In this research, the effect of low-fare entry on incumbent network carriers is examined, with special focus on the impacts of entry on traditional measures of airline performance. An analysis of various markets with low-fare competition highlights the typical effects of low-fare entry on these traditional aggregate measures. In a thorough analysis of two specific cases, we show that these measures, although affected similarly by entry, were very poor predictors of the new entrant's success in these markets, and inadequate indicators of incumbent response. AirTran successfully entered the Atlanta-Orlando market, while Spirit failed to maintain its operations in the Detroit-Boston market. We highlight the differences between these two markets and explain why the performance of these two carriers was so different. In a second part, a simulator of competitive airline networks--the Passenger Origin Destination Simulator--is used to model various scenarios of entry in a single market environment so as to determine<br>by Thomas O. Gorin.<br>Ph.D.

The purpose of this paper is to assess consumer behavior in the airline industry from a perspective beyond the effects of price discrimination. First the consequences of dynamic pricing will be assessed before looking at the role of social media and offline social influences, consumer satisfaction and airline equilibrium networks and their effects on consumer loyalty. Final implications on aviation revenue management will be drawn.

We develop and evaluate two significant modeling concepts within the context of a large-scale Airspace Planning and Collaborative Decision-Making Model (APCDM) and, thereby, enhance its current functionality in support of both strategic and tactical level flight assessments. The first major concept is a new severe weather-modeling paradigm that can be used to assess existing tactical en route flight plan strategies such as the Flight Management System (FMS) as well as to provide rerouting strategies. The second major concept concerns modeling the mediated bartering of slot exchanges involving airline trade offers for arrival/departure slots at an arrival airport that is affected by the Ground Delay Program (GDP), while simultaneously considering issues related to sector workloads, airspace conflicts, as well as overall equity concerns among the airlines. This research effort is part of an $11.5B, 10-year, Federal Aviation Administration (FAA)-sponsored program to increase the U.S. National Airspace (NAS) capacity by 30 percent by the year 2010. Our innovative contributions of this research with respect to the severe weather rerouting include (a) the concept of â Probability-Netsâ and the development of discretized representations of various weather phenomena that affect aviation operations; (b) the integration of readily accessible severe weather probabilities from existing weather forecast data provided by the National Weather Service (NWS); (c) the generation of flight plans that circumvent severe weather phenomena with specified probability levels, and (d) a probabilistic delay assessment methodology for evaluating planned flight routes that might encounter potentially disruptive weather along its trajectory. Given a fixed set of reporting stations from the CONUS Model Output Statistics (MOS), we begin by constructing weather-specific probability-nets that are dynamic with respect to time and space. Essential to the construction of the probability-nets are the point-by-point forecast probabilities associated with MOS reporting sites throughout the United States. Connections between the MOS reporting sites form the strands within the probability-nets, and are constructed based upon a user-defined adjacency threshold, which is defined as the maximum allowable great circle distance between any such pair of sites. When a flight plan traverses through a probability-net, we extract probability data corresponding to the points where the flight plan and the probability-net strand(s) intersect. The ability to quickly extract this trajectory-related probability data is critical to our weather-based rerouting concepts and the derived expected delay and related cost computations in support of the decision-making process. Next, we consider the superimposition of a flight-trajectory-grid network upon the probability-nets. Using the U.S. Navigational Aids (Navaids) as the network nodes, we develop an approach to generate flight plans that can circumvent severe weather phenomena with specified probability levels based on determining restricted, time-dependent shortest paths between the origin and destination airports. By generating alternative flight plans pertaining to specified threshold strand probabilities, we prescribe a methodology for computing appropriate expected weather delays and related disruption factors for inclusion within the APCDM model. We conclude our severe weather-modeling research by conducting an economic benefit analysis using a k-means clustering mechanism in concert with our delay assessment methodology in order to evaluate delay costs and system disruptions associated with variations in probability-net refinement-based information. As a flight passes through the probability-net(s), we can generate a probability-footprint that acts as a record of the strand intersections and the associated probabilities from origin to destination. A flight planâ s probability-footprint will differ for each level of data refinement, from whence we construct route-dependent scenarios and, subsequently, compute expected weather delay costs for each scenario for comparative purposes. Our second major contribution is the development of a novel slot-exchange modeling concept within the APCDM model that incorporates various practical issues pertaining to the Ground Delay Program (GDP), a principal feature in the FAAâ s adoption of the Collaborative Decision-Making (CDM) paradigm. The key ideas introduced here include innovative model formulations and several new equity concepts that examine the impact of â at-least, at-mostâ trade offers on the entire mix of resulting flight plans from respective origins to destinations, while focusing on achieving defined measures of â fairnessâ with respect to the selected slot exchanges. The idea is to permit airlines to barter assigned slots at airports affected by the Ground Delay Program to their mutual advantage, with the FAA acting as a mediator, while being cognizant of the overall effect of the resulting mix of flight plans on air traffic control sector workloads, collision risk and safety, and equity considerations. We start by developing two separate slot-exchange approaches. The first consists of an external approach in which we formulate a model for generating a set of package-deals, where each package-deal represents a potential slot-exchange solution. These package-deals are then embedded within the APCDM model. We further tighten the model representation using maximal clique cover-based cuts that relate to the joint compatibility among the individual package-deals. The second approach significantly improves the overall model efficiency by automatically generating package-deals as required within the APCDM model itself. The model output prescribes a set of equitable flight plans based on admissible trades and exchanges of assigned slots, which are in addition conformant with sector workload capabilities and conflict risk restrictions. The net reduction in passenger-minutes of delay for each airline is the primary metric used to assess and compare model solutions. Appropriate constraints are included in the model to ensure that the generated slot exchanges induce nonnegative values of this realized net reduction for each airline. In keeping with the spirit of the FAAâ s CDM initiative, we next propose four alternative equity methods that are predicated on different specified performance ratios and related efficiency functions. These four methods respectively address equity with respect to slot-exchange-related measures such as total average delay, net delay savings, proportion of acceptable moves, and suitable value function realizations. For our computational experiments, we constructed several scenarios using real data obtained from the FAA based on the Enhanced Traffic Management System (ETMS) flight information pertaining to the Miami and Jacksonville Air Route Traffic Control Centers (ARTCC). Through our experimentation, we provide insights into the effect of the different proposed modeling concepts and study the sensitivity with respect to certain key parameters. In particular, we compare the alternative proposed equity formulations by evaluating their corresponding slot-exchange solutions with respect to the net reduction in passenger-minutes of delay for each airline. Additionally, we evaluate and compare the computational-effort performance, under both time limits and optimality thresholds, for each equity method in order to assess the efficiency of the model. The four slot-exchange-based equity formulations, in conjunction with the internal slot-exchange mechanisms, demonstrate significant net savings in computational effort ranging from 25% to 86% over the original APCDM model equity formulation. The model has been implemented using Microsoft Visual C++ and evaluated using a C++ interface with CPLEX 9.0. The overall results indicate that the proposed modeling concepts offer viable tools that can be used by the FAA in a timely fashion for both tactical purposes, as well as for exploring various strategic issues such as air traffic control policy evaluations; dynamic airspace resectorization strategies as a function of severe weather probabilities; and flight plan generation in response to various disruption scenarios.<br>Ph. D.

In recent years European airspace has become increasingly congested and airlines can now observe that en-route capacity constraints are the fastest growing source of flight delays. In 2010 this source of delay accounted for 19% of all flight delays in Europe and has been increasing with an average yearly rate of 17% from 2005 to 2010. This paper suggests and evaluates an approach to how disruption management can be combined with flight planning in order to create more proactive handling of the kind of disruptions, which are caused by congested airspace. The approach is evaluated using data from a medium size European carrier and estimates a lower bound saving of several million USD.

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2019, In conjunction with the Leaders for Global Operations Program at MIT<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2019, In conjunction with the Leaders for Global Operations Program at MIT<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 100-107).<br>On average, an airline starts to place orders or aircraft within 3 - 10 years before the expected delivery date. During this time, there could be changes given the natural response of the airlines to continuously refine their fleet plan. This behavior implies many possible scenarios that aircraft manufacturers would like to understand and predict in order to improve their backlog management initiatives. Furthermore, demand estimation is always a powerful lever in any production system because it allows the manufacturer to be prepared to address the customer's needs. An airline's network and fleet are dependent on each other. The network is highly dependent on the capabilities of the available fleet but also, the fleet is built considering the network strategy of an airline. Giving this relationship this project aims to develop a set of predictive models based on network-related variables that allow to forecast the RPK growth of an airline in the following 7 years. Most of the available forecast for air passenger traffic focus on economic variables such as fuel price, GDP of the countries, trade index and population among others. This project wanted to explore if network variables had any relationship with future RPKs for an airline. After the analysis of historical data of more than 400 carriers from 2010 to 2017, the results show that although mild, there is an influence of these variables and we could use the resulting forecast with a solid reliability. Furthermore, the final coefficients show more influence of these variables for short-haul (less than 2500 nautical miles) and Economy markets than long-haul and Business markets. For Boeing and its current backlog size of more than 5,800 aircraft [1], the resulting models represent another tool that will aid the company in making data driven decisions regarding aircraft production, new orders to come, evaluation of current and potential customers, and other business analysis.<br>by Norányeli Paola Molina Realpe.<br>M.B.A.<br>S.M.<br>M.B.A. Massachusetts Institute of Technology, Sloan School of Management<br>S.M. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics

Improving the predictability of airline schedules in the National Airspace System (NAS) has been a constant endeavor, particularly as system delays grow with ever-increasing demand. Airline schedules need to be resistant to perturbations in the system including Ground Delay Programs (GDPs) and inclement weather. The strategic search heuristic proposed in this dissertation significantly improves airline schedule reliability by assigning airport departure and arrival slots to each flight in the schedule across a network of airports. This is performed using a multi-objective optimization approach that is primarily based on historical flight and taxi times but also includes certain airline, airport, and FAA priorities. The intent of this algorithm is to produce a more reliable, robust schedule that operates in today's environment as well as tomorrow's 4-Dimensional Trajectory Controlled system as described the FAA's Next Generation ATM system (NextGen). This novel airline schedule optimization approach is implemented using a multi-objective evolutionary algorithm which is capable of incorporating limited airport capacities. The core of the fitness function is an extensive database of historic operating times for flight and ground operations collected over a two year period based on ASDI and BTS data. Empirical distributions based on this data reflect the probability that flights encounter various flight and taxi times. The fitness function also adds the ability to define priorities for certain flights based on aircraft size, flight time, and airline usage. The algorithm is applied to airline schedules for two primary US airports: Chicago O'Hare and Atlanta Hartsfield-Jackson. The effects of this multi-objective schedule optimization are evaluated in a variety of scenarios including periods of high, medium, and low demand. The schedules generated by the optimization algorithm were evaluated using a simple queuing simulation model implemented in AnyLogic. The scenarios were simulated in AnyLogic using two basic setups: (1) using modes of flight and taxi times that reflect highly predictable 4-Dimensional Trajectory Control operations and (2) using full distributions of flight and taxi times reflecting current day operations. The simulation analysis showed significant improvements in reliability as measured by the mean square difference (MSD) of filed versus simulated flight arrival and departure times. Arrivals showed the most consistent improvements of up to 80% in on-time performance (OTP). Departures showed reduced overall improvements, particularly when the optimization was performed without the consideration of airport capacity. The 4-Dimensional Trajectory Control environment more than doubled the on-time performance of departures over the current day, more chaotic scenarios. This research shows that airline schedule reliability can be significantly improved over a network of airports using historical flight and taxi time data. It also provides for a mechanism to prioritize flights based on various airline, airport, and ATC goals. The algorithm is shown to work in today's environment as well as tomorrow's NextGen 4-Dimensional Trajectory Control setup.<br>Ph.D.<br>Department of Industrial Engineering and Management Systems<br>Engineering and Computer Science<br>Industrial Engineering PhD

博士<br>國立中央大學<br>土木工程研究所<br>88<br>This thesis address the problem of determining nested control policies, when different fare classes and multiple origin-destination itineraries on airline networks do not arrive in sequential blocks. Based on the airline booking process, we formulated this problem as a two-state decision problem with network recourses, which involves one optimal seat pre-allocation master problem and several single itinerary booking control parameters sub-problems. According to the optimal decision control rule of booking control, we first solved the single OD itinerary multiple fare class seat inventory management problems to exclude potentially redundant demand and used network skill to solve the multiple itinerary seat allocation problems. Whole this acts as a reference for booking control. The network sensitivity is then employed to deduce the bunch nested booking limits and real-time re-optimal control parameters within a single itinerary or the entire network. In the case of demand overlay, we adjusted the updating procedure for the related control parameters in robust form and developed a simulation scheme with eight different scenario patterns. The preliminary test results were good.

abstract: Revenue management is at the core of airline operations today; proprietary algorithms and heuristics are used to determine prices and availability of tickets on an almost-continuous basis. While initial developments in revenue management were motivated by industry practice, later developments overcoming fundamental omissions from earlier models show significant improvement, despite their focus on relatively esoteric aspects of the problem, and have limited potential for practical use due to computational requirements. This dissertation attempts to address various modeling and computational issues, introducing realistic choice-based demand revenue management models. In particular, this work introduces two optimization formulations alongside a choice-based demand modeling framework, improving on the methods that choice-based revenue management literature has created to date, by providing sensible models for airline implementation. The first model offers an alternative formulation to the traditional choice-based revenue management problem presented in the literature, and provides substantial gains in expected revenue while limiting the problem’s computational complexity. Making assumptions on passenger demand, the Choice-based Mixed Integer Program (CMIP) provides a significantly more compact formulation when compared to other choice-based revenue management models, and consistently outperforms previous models. Despite the prevalence of choice-based revenue management models in literature, the assumptions made on purchasing behavior inhibit researchers to create models that properly reflect passenger sensitivities to various ticket attributes, such as price, number of stops, and flexibility options. This dissertation introduces a general framework for airline choice-based demand modeling that takes into account various ticket attributes in addition to price, providing a framework for revenue management models to relate airline companies’ product design strategies to the practice of revenue management through decisions on ticket availability and price. Finally, this dissertation introduces a mixed integer non-linear programming formulation for airline revenue management that accommodates the possibility of simultaneously setting prices and availabilities on a network. Traditional revenue management models primarily focus on availability, only, forcing secondary models to optimize prices. The Price-dynamic Choice-based Mixed Integer Program (PCMIP) eliminates this two-step process, aligning passenger purchase behavior with revenue management policies, and is shown to outperform previously developed models, providing a new frontier of research in airline revenue management.<br>Dissertation/Thesis<br>Doctoral Dissertation Industrial Engineering 2016

After the complete liberalization of the airline industry during the 1990s the industry has faced a rapid growth in passenger numbers. This has mainly been caused by the emergence of so-called Low Cost Carrier (LCC) that offer a simplified product (i.e. point-to-point flights without any frills) at a lower cost than traditional Network Carriers. Furthermore LCC also introduced a less differentiated pricing structure (Restriction Free Pricing) which forced competing network carriers to reduce the degree of price discrimination which they were able to practice until then in order to defend their market shares. This has led to a decrease of average yields, which resulted in difficulties for (smaller) Network Carriers to cover their fixed costs, related to the operation of a hub & spoke network. In this environment network airlines are looking for new revenue sources as well as further sources of cost reduction. This development has amplified the consolidation trend of the airline industry and led to the emergence of several multi-hub networks (e.g. Lufthansa runs hub-operation in Frankfurt, Munich, Zurich and Vienna). One way to leverage the fact that multi-hub networks allow several routings for one origin-destination city pair would be the introduction of flexible tickets, where the actual routing of the passenger is not defined at the moment of purchase but only a certain time prior to departure. This allows airlines to raise the load factor on their network by increasing the degree of overbooking which they currently practice by pooling the risk that more passengers arrive than there is capacity among several flights. Furthermore these tickets might allow network carriers to compete in the low-cost-airline segment without having to further reduce the price level of their regular product (with specified routing). The present dissertation examined possible designs of such a ticket and their impact on the acceptance by passengers by means of a choice based conjoint study among 356 travelers. The findings suggest that while 77.5% of leisure travelers are willing to accept flexible time-range tickets in their relevant set, only 56% of business travelers are considering using this kind of ticket. More particular the results also showed that business travelers are not willing to compromise on travel duration and departure times, and are subsequently willing to pay a premium for specified tickets. A market share simulation showed that depending on the selected product layout flexible time-range tickets are able to gain up to 60% market share when offered at a discount of up to 33% relative to traditional tickets. When it comes to the actual layout, the largest lever to increase the acceptance is to exclude connection flights from the potential set of flights. The results contribute to the young research area on flexible products by assessing the disutility which is experienced by customers with regard to particular product characteristics of flexible products. Furthermore the results aim at providing airline managers with a comprehensive overview of the possibilities which flexible time-range tickets bring along when it comes to increasing the load factor and thereby the revenues in a multi-hub network. (author's abstract)

碩士<br>國立交通大學<br>運輸科技與管理學系<br>95<br>Since American Airlines successfully applied revenue management (RM) to raise its revenue, RM has become a common technique in the airline industry. Due to the current hub-and-spoke operation, the focus of the RM research has shifted from the traditional single-leg problem to the network-type problem. It was noticed that, though a mainstream approach for the network RM problem, the bid price control is ill with complicated procedure and heavy computational load. In addition, there are considerable limitations for this approach in terms of both methodological theory and practical application. 　　Based on the dynamic programming (DP) approach, which generates the optimal control policy, this study develops a method that can generate a suitable seat control policy by approximating the expected revenue function in the DP model. First, this study establishes the model and the associated algorithm for the single-leg RM problem. After performing the mathematical analysis, this approach is extended to the network RM problem. Finally, in order deal with the RM problem under the network context, this study adopts the concept of sampling to generate the approximation function. 　　Based on the result in the numerical experiment of a two-leg numerical example, , the validity and effectiveness of the developed method is verified as the generated solution which is pretty close to the optimal solution and significantly better than the case with no RM control. It is believed that this study should serve as an excellent alternative for the current bid price control and provide an inspiring concept for other network-related RM problems.

Keywords. Hub-to-hub network, bid-price control, certainty equivalent control, combinatorial optimization, structures, primal-dual, revenue management, airline network, monotone thresholds, supermodularity/submodularity, L&natur; concavity, Lagrange dual.<br>The subject of this study is the revenue management problem in hub-to-hub airline networks. The network consists of two hubs and a connecting flight between them with spoke cities expanding outwards. The airline produces various itineraries within the network, and its flights compete with each other for limited flight capacities during a fixed booking period. Although stochastic dynamic network revenue management has been theoretically established, in reality its implementation is still heavily dependent on linear programming-based heuristics. Simpson (1989) and Williamson (1992) proposed bid price control, which is now widely adopted by major airlines. Bertsimas and de Boer (2003) proposed certainty equivalent control, which has been little studied by RM researchers. In this thesis, bid price control is first explained, and then the structural properties of the hub-to-hub network are investigated. Using the Lagrange dual-function and the primal-dual relationship, it is shown that the threshold values used in bid price control have some monotone properties in the network's capacity states. The certainty equivalent control is then applied to the hub-to-hub network. By linking the network revenue management problem with a maximum-weight circulation problem in network flow, the optimal value function is shown to be supermodular in certain capacity dimensions, and submodular in other dimensions. This leads to the monotonicity of CEC thresholds on some short-haul itineraries. The notion of L &natur; concavity developed by Murota and Shioura (2005) is applied to this work, and it is shown that even the CEC thresholds on some two-leg or three-leg long-haul itineraries are monotonically increasing or decreasing in certain legs' capacities. It is hoped that the new structural properties found in this thesis can lead to a reduction of the computational work in the implementation of both the bid price control and the certainty equivalent control in the hub-to-hub airline network.<br>He, Hongzhi.<br>Adviser: Zhang Shuzhong.<br>Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: .<br>Thesis (Ph.D.)--Chinese University of Hong Kong, 2010.<br>Includes bibliographical references (leaves 111-118).<br>Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.<br>Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.<br>Abstract also in Chinese.

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação<br>In recent years, social networks have assumed an important role in life of millions of people and more recently in life of organizations, that are discovering their potential. We are living in a context of a generalized crisis in Europe and in the world but we are also live in a globalized Europe an world, where achieving competitive advantage depends much of means either than productive ones. Some of this factors have evolve and are now almost inseparable of organizations allowing to built holistic vision of their systems people, information and equipments. Knowledge management, information management and business process management are recognized as a source of competitive advantage as well, but just like social network not all organizations already understand their potential. For the development of the thesis we did a qualitative research based on a literature review and a case study on a Portuguese airline whose key findings are centred on the fact that social networks, by bring voices that are dispersed, enable to organizations to have more and better knowledge which can be used in process redesign and thus achieving competitive advantage. This study contributes to understand how SN can help organizations improve their Business Process and how Business Process Management (BPM), Knowledge, Social Computing and Social BPM, interact and influence each other and help organizations optimize their processes.