<p>  </p> <p>With rising concerns over commercial aviation’s contribution to global carbon emissions, there exists a tremendous pressure on the aviation industry to find advanced technological solutions to reduce its share of CO2 emissions. Single-aisle (or narrowbody) aircraft are the biggest contributors to CO2 emissions by number of operations, insisting a need to reduce / eliminate their aircraft-level fuel consumption as soon as possible. A potential solution for this is to operate fully-electric single-aisle aircraft; however, the limitations of the current (and predicted future) battery technology is forcing the industry to explore hybrid-electric aircraft as a possible mid-term solution.</p> <p>Modeling hybrid-electric aircraft comes with its own challenges due to the presence of two different propulsion sources – gas turbine engines (powered by Jet-A fuel) and electric motors (powered by batteries). Since traditional sizing approaches and legacy sizing tools do not seem to work well for hybrid-electric aircraft, this work presents a “flight-mechanics-based” conceptual sizing tool for hybrid-electric aircraft, set up as a Multidisciplinary Design Optimization (MDO) toolbox. Some of the key features of the sizing tool include concurrently sizing the electric motors and downsizing the gas turbine engines while meeting the one-engine-inoperative (OEI) and top-of-climb constraints, and re-sizing the fuselage to account for the volumetric constraints associated with required batteries.</p> <p>Current work considers a parallel hybrid-electric single-aisle aircraft with a 900 nmi design range, with electric power augmentation (with electric motors operating at full throttle) available only for the takeoff and climb segments when sizing the aircraft. Four hybrid-electric propulsion technology cases are considered, and the resulting hybrid-electric aircraft show 15.0% to 22.5% reduction in fuel burn compared to a Boeing 737-800 aircraft.</p> <p>Another challenge with modeling hybrid-electric aircraft is determining their off-design performance characteristics (considering a different payload or mission range, or both). This work presents an energy management tool – set up as a nonlinear programming optimization problem – to minimize the fuel burn for a payload-range combination by identifying the optimal combination of throttle settings for the gas turbine engines and the electric motors during takeoff, climb, and cruise, along with identifying an optimal flight path. The energy management tool enables fuel savings of at least of 2%, with actual savings ranging from 142.1 lbs to 276.1 lbs per trip for a sample route (LGA–ORD) at a 80% load factor.</p> <p>Although the hybrid-electric aircraft sizing and performance analysis studies show encouraging results about the potential reduction in carbon emissions at an aircraft level, the future fleet-level carbon emissions are not expected to reduce proportionally to these aircraft level emission reductions. This work predicts the fleet-level environmental impacts of future single-aisle parallel hybrid-electric aircraft by modeling the behavior of a profit-seeking airline (with a mixture of conventional all Jet-A fuel burning and hybrid electric aircraft in its fleet) using the Fleet-Level Environmental Evaluation Tool (FLEET). FLEET’s model-based predictions rely upon historically-based information about US-touching airline routes and passenger demand served by US flag-carrier airlines from the Bureau of Transportation Statistics to initiate model-based predictions of future demand, aircraft fleet mix, and aircraft operations. Using the aircraft performance coefficients from the energy management tool to represent the behavior of a single-aisle parallel hybrid-electric aircraft, the FLEET simulation predicts the changes in the fleet-wide carbon emissions due to the introduction of this new aircraft in an airline fleet in the year 2035. By 2055, FLEET results predict that the fleet-wide CO2 emissions with hybrid-electric aircraft in the fleet mix are at least 1.2% lower than the fleet-wide CO2 emissions of a conventional (all Jet-A fuel burning) aircraft-only airline. The rather limited reduction in emissions is an attribute of the reduced range capability and higher operating cost of the hybrid-electric aircraft (relative to a conventional aircraft of similar size). This causes the airline to change the usage, acquisition and retirement of its conventional aircraft when hybrid-electric aircraft are available; this is most notable to serve passenger demand on certain predominantly single-aisle service routes that cannot be flown by the future single-aisle hybrid-electric aircraft. </p>

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No / The widespread protests of November 2019 may be marked as the bloodiest recent chapter of the Islamic Republic of Iran's history in terms of popular dissent. The two major protests in December 2017 and November 2019, followed by the public reaction to the shooting down of the Ukrainian International Airlines Flight 752 by the IRGC over Tehran after the US killing of General Soleimani, suggest that the prevailing dynamics of political protest in Iran are changing. There is an increasing sense of radicalisation among protesters, while the state is prepared to resort to extreme violence to maintain control. The geography of political protest has changed. The declining economic situation has had a profound impact on the more vulnerable segments of the society who are now increasingly playing a more proactive role in challenging the state. The methods of protest have been evolving over the last four decades, especially in the cultural arena. Last but not least, the willingness of the protesters both to endure and inflict violence is precipitously transforming state-society relations beyond recognition. This article begins by providing a brief overview of protest in the history of the Islamic Republic, up to the public reaction to the 2020 downing of the Ukrainian airline over Tehran. This provides a historical context to assess the ways in which both the political climate and protests have changed over the last four decades. A section identifying and analysing the factors which have created the current political cul-de-sac then follows. The changing dynamics of the protests are the result of the existing political gridlock and the economic crisis, and it is thus important to evaluate the prevailing conditions which have paved the way for the radicalisation of political climate in Iran. The final section examines the changing dynamics of political protest.

Stochastic programming is an optimization technique that incorporates random variables as parameters. Because it better reflects the uncertain real world than its traditional deterministic counterpart, stochastic programming has drawn increasingly more attention among decision-makers, and its applications span many fields including financial engineering, health care, communication systems, and supply chain management. On the flip side, stochastic programs are usually very difficult to solve, which is further compounded by the fact that in many of the aforementioned applications, we also have discrete decisions, thereby rendering these problems even more challenging. In this dissertation, we study the class of two-stage stochastic mixed-integer programs (SMIP), which, as its name suggests, lies at the confluence of two formidable classes of problems. We design a novel algorithm for this class of problems, and also explore specialized approaches for two related real-world applications. Although a number of algorithms have been developed to solve two-stage SMIPs, most of them deal with problems containing purely integer or continuous variables in either or both of the two stages, and frequently require the technology and/or recourse matrices to be deterministic. As a ground-breaking effort, in this work, we address the challenging class of two-stage SMIPs that involve 0-1 mixed-integer variables in both stages. The only earlier work on solving such problems (Car&#248;e and Schultz (1999)) requires the optimization of several non-smooth Lagrangian dual problems using subgradient methods in the bounding process, which turns out to be computationally very expensive. We begin with proposing a decomposition-based branch-and-bound (DBAB) algorithm for solving two-stage stochastic programs having 0-1 mixed-integer variables in both stages. Since the second-stage problems contain binary variables, their value functions are in general nonconvex and discontinuous; hence, the classical Benders' decomposition approach (or the L-shaped method) for solving two-stage stochastic programs, which requires convex subproblem value functions, cannot be directly applied. This motivates us to relax the second-stage problems and accompany this relaxation with a convexification process. To make this process computationally efficient, we propose to construct a certain partial convex hull representation of the two-stage solution space, using the relaxed second-stage constraints and the restrictions confining the first-stage variables to lie within some hyperrectangle. This partial convex hull is sequentially generated using a convexification scheme, such as the Reformulation-Linearization Technique (RLT), which yields valid inequalities that are functions of the first-stage variables and, of noteworthy importance, are reusable in the subsequent subproblems by updating the values of the first-stage variables. Meanwhile, since the first stage contains continuous variables, whenever we tentatively fix these variables at some given feasible values, the resulting constraints may not be facial with respect to the associated bounding constraints that are used to construct the partial convex hull. As a result, the constructed Benders' subproblems define lower bounds for the second-stage value functions, and likewise, the resulting Benders' master problem provides a lower bound for the original stochastic program defined over the same hyperrectangle. Another difficulty resulting from continuous first-stage variables is that when the given first-stage solution is not extremal with respect to its bounds, the second-stage solution obtained for a Benders' subproblem defined with respect to a partial convex hull representation in the two-stage space may not satisfy the model's binary restrictions. We thus need to be able to detect whether or not a Benders' subproblem is solved by a given fractional second-stage solution. We design a novel procedure to check this situation in the overall algorithmic scheme. A key property established, which ensures global convergence, is that these lower bounds become exact if the given first-stage solution is a vertex of the defining hyperrectangle, or if the second-stage solution satisfies the binary restrictions. Based on these algorithmic constructs, we design a branch-and-bound procedure where the branching process performs a hyperrectangular partitioning of the projected space of the first-stage variables, and lower bounds for the nodal problems are computed by applying the proposed modified Benders' decomposition method. We prove that, when using the least-lower-bound node-selection rule, this algorithm converges to a global optimal solution. We also show that the derived RLT cuts are not only reusable in subsequent Benders iterations at the same node, but are also inheritable by the subproblems of the children nodes. Likewise, the Benders' cuts derived for a given sub-hyperrectangle can also be inherited by the lower bounding master programs solved for its children nodes. Using these cut inheritance properties results in significant savings in the overall computational effort. Some numerical examples and computational results are presented to demonstrate the efficacy of this approach. The sizes of the deterministic equivalent of our test problems range from having 386 continuous variables, 386 binary variables, and 386 constraints, up to 1795 continuous variables, 1539 binary variables, and 1028 constraints. The results reveal an average savings in computational effort by a factor of 9.5 in comparison with using a commercial mixed-integer programming package (CPLEX 8.1) on a deterministic equivalent formulation. We then explore an important application of SMIP to enhance the traditional airline fleet assignment models (FAM). Given a flight schedule network, the fleet assignment problem solved by airline companies is concerned with assigning aircraft to flight legs in order to maximize profit with respect to captured path- or itinerary-based demand. Because certain related crew scheduling regulations require early information regarding the type of aircraft serving each flight leg, the current practice adopted by airlines is to solve the fleet assignment problem using estimated demand data 10-12 weeks in advance of departure. Given the level of uncertainty, deterministic models at this early stage are inadequate to obtain a good match of aircraft capacity with passenger demands, and revisions to the initial fleet assignment become naturally pertinent when the observed demand differs considerably from the assigned aircraft capacities. From this viewpoint, the initial decision should embrace various market scenarios so that it incorporates a sufficient look-ahead feature and provides sufficient flexibility for the subsequent re-fleeting processes to accommodate the inevitable demand fluctuations. With this motivation, we propose a two-stage stochastic programming approach in which the first stage is concerned with the initial fleet assignment decisions and, unlike the traditional deterministic methodology, focuses on making only a family-level assignment to each flight leg. The second stage subsequently performs the detailed assignments of fleet types within the allotted family to each leg under each of the multiple potential scenarios that address corresponding path- or itinerary-based demands. In this fashion, the initial decision of what aircraft family should serve each flight leg accomplishes the purpose of facilitating the necessary crew scheduling decisions, while judiciously examining the outcome of future re-fleeting actions based on different possible demand scenarios. Hence, when the actual re-fleeting process is enacted several weeks later, this anticipatory initial family-level assignment will hopefully provide an improved overall fleet type re-allocation that better matches demand. This two-stage stochastic model is complemented with a secondary model that performs adjustments within each family, if necessary, to provide a consistent fleet type-assignment information for accompanying decision processes, such as yield management. We also propose several enhanced fleet assignment models, including a robust optimization model that controls decision variation among scenarios and a stochastic programming model that considers the recapture effect of spilled demand. In addition to the above modeling concepts and framework, we also contribute in developing effective solution approaches for the proposed model, which is a large-scale two-stage stochastic 0-1 mixed-integer program. Because the most pertinent information needed from the initial fleet assignment is at the family level, and the type-level assignment is subject to change at the re-fleeting stage according to future demand realizations, our solution approach focuses on assigning aircraft families to the different legs in the flight network at the first stage, while finding relaxed second-stage solutions under different demand scenarios. Based on a polyhedral study of a subsystem extracted from the original model, we derive certain higher-dimensional convex hull as well as partial convex hull representations for this subsystem. Accordingly, we propose two variants for the primary model, both of which relax the binary restrictions on the second-stage variables, but where the second variant then also accommodates the partial convex hull representations, yielding a tighter, albeit larger, relaxation. For each variant, we design a suitable solution approach predicated on Benders' decomposition methodology. Using certain realistic large-scale flight network test problems having 900 flight legs and 1,814 paths, as obtained from United Airlines, the proposed stochastic modeling approach was demonstrated to increase daily expected profits by about 3% (which translates to about $160 million per year) in comparison with the traditional deterministic model in present usage, which considers only the expected demand. Only 1.6% of the second-stage binary variables turn out to be fractional in the first variant, and this number is further reduced to 1.2% by using the tighter variant. Furthermore, when attempting to solve the deterministic equivalent formulation for these two variants using a commercial mixed-integer programming package (CPLEX 8.1), both the corresponding runs were terminated after reaching a 25-hour cpu time limit. At termination, the software was still processing the initial LP relaxation at the root node for each of these runs, and no feasible basis was found. Using the proposed algorithms, on the other hand, the solution times were significantly reduced to 5 and 19 hours for the two variants, respectively. Considering that the fleet assignment models are solved around three months in advance of departure, this solution time is well acceptable at this early planning stage, and the improved quality in the solution produced by considering the stochasticity in the system is indeed highly desirable. Finally, we address another practical workforce planning problem encountered by a global financial firm that seeks to manage multi-category workforce for functional areas located at different service centers, each having office-space and recruitment-capacity constraints. The workforce demand fluctuates over time due to market uncertainty and dynamic project requirements. To hedge against the demand fluctuations and the inherent uncertainty, we propose a two-stage stochastic programming model where the first stage makes personnel recruiting and allocation decisions, while the second stage, based on the given personnel decision and realized workforce demand, decides on the project implementation assignment. The second stage of the proposed model contains binary variables that are used to compute and also limit the number of changes to the original plan. Since these variables are concerned with only one quality aspect of the resulting workforce plan and do not affect feasibility issues, we replace these binary variables with certain conservative policies regarding workforce assignment change restrictions in order to obtain more manageable subproblems that contain purely continuous variables. Numerical experiments reveal that the stochastic programming approach results in significantly fewer alterations to the original workforce plan. When using a commercial linear programming package CPLEX 9.0 to solve the deterministic equivalent form directly, except for a few small-sized problems, this software failed to produce solutions due to memory limitations, while the proposed Benders' decomposition-based solution approach consistently solved all the practical-sized test problems with reasonable effort. To summarize, this dissertation provides a significant advancement in the algorithmic development for solving two-stage stochastic mixed-integer programs having 0-1 mixed-integer variables in both stages, as well as in its application to two important contemporary real-world applications. The framework for the proposed solution approaches is to formulate tighter relaxations via partial convex hull representations and to exploit the resulting structure using suitable decomposition methods. As decision robustness is becoming increasingly relevant from an economic viewpoint, and as computer technological advances provide decision-makers the ability to explore a wide variety of scenarios, we hope that the proposed algorithms will have a notable positive impact on solving stochastic mixed-integer programs. In particular, the proposed stochastic programming airline fleet assignment and the workforce planning approaches studied herein are well-poised to enhance the profitability and robustness of decisions made in the related industries, and we hope that similar improvements are adapted by more industries where decisions need to be made in the light of data that is shrouded by uncertainty. / Ph. D.

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 "Probability-Nets" 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 plan'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 FAA'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 "at-least, at-most" trade offers on the entire mix of resulting flight plans from respective origins to destinations, while focusing on achieving defined measures of "fairness" 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 FAA'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. / Ph. D.

We develop a detailed, large-scale, airspace planning and collaborative decision-making model (APCDM), that is part of an $11.5B, 10-year, Federal Aviation Administration (FAA)-sponsored effort to increase U.S. National Airspace (NAS) capacity by 30 percent. Given a set of flights that must be scheduled during some planning horizon, we use a mixed-integer programming formulation to select a set of flight plans from among alternatives subject to flight safety, air traffic control workload, and airline equity constraints. Novel contributions of this research include three-dimensional probabilistic conflict analyses, the derivation of valid inequalities to tighten the conflict safety representation constraints, the development of workload metrics based on average (and its variance from) peak load measures, and the consideration of equity among airline carriers in absorbing the costs related to re-routing, delays, and cancellations. We also propose an improved set of flight plan cost factors for representing system costs and investigating fairness issues by addressing flight dependencies occurring in hubbed operations, as well as market factors such as schedule convenience, reliability, and the timeliness of connections. The APCDM model has potential use for both tactical and strategic applications, such as air traffic control in response to severe weather phenomenon or spacecraft launches, FAA policy evaluation, Homeland Defense contingency planning, and military air campaign planning. The model is tested to consider various airspace restriction scenarios imposed by dynamic severe weather systems and space launch Special Use Airspace (SUA) impositions. The results from this model can also serve to augment the FAA's National Playbook of standardized flight profiles in different disruption-prone regions of the National Airspace. / Ph. D.

Power imbalances and gender-based violence (GBV) have increasingly been cited as important determinants putting women at risk of HIV infections. Studies have shown that globally one in every three women has been beaten, coerced into sex or otherwise abused in her lifetime. The study explored working women’s perceptions of power, gender-based violence and HIV-infection risks. A qualitative, explorative study was conducted among female employees in an airline business in Namibia. Five women participated in in-depth, face-to-face interviews. The findings show that all of the participants experienced power imbalances and GBV in their intimate relationships. All of the women reported emotional or psychological abuse, whilst the majority were subjected to economic abuse, followed by physical abuse, and two alleged having been sexually abused. The study concludes with specific recommendations for the development and successful implementation of workplace policy and programmes to protect and promote women’s rights. / Social Work / M.A. (Social Behaviour Studies in HIV/AIDS)

Study project (MEcon) -- University of Stellenbosch, 2000. / ENGLISH ABSTRACT: This assignment explains the search for a mechanism that can increase tourism flow by improved utilisation of airline capacity. The inherent characteristics of air transport indicate that the industry is subject to low short term marginal costs and that it is very tempting to award discount tariffs for last minute bookings. The challenge to management is to attract new passengers with discount tariffs without loosing full tariff passengers. Travel clubs are one of the mechanisms that are utilised to achieve aforementioned objective. These clubs offer mainly discount tariffs on hotel accommodation, car hire and airline tickets to their members. The acceptability of a travel club that applies restricting measures such as for example short notice periods, adaptable depart and return dates and shortened lead times have been tested in the South African market. The majority of respondents surveyed were in favour of such a travel club. An important finding is that South Africans are prepared to travel in a chosen month but that the travel dates within that month are adaptable in exchange for discount tariffs. The research also indicate that the availability of funds was decisive in the decision to travel or not to travel over seas. In order to overcome this problem the introduction of a providence account is recommended as part of the travel club's products. The challenge for the travel club is thus to consolidate the demand and to match it with the excess airline capacity. / AFRIKAANSE OPSOMMING: Hierdie werkstuk beskryf die soeke na 'n meganisme wat toerismevloei kan verhoog deur die verbeterde kapasiteitsbenutting van lugvervoer. Die inherente kenmerke van lugvervoer toon dat die bedryf onderhewig is aan lae korttermyn marginale koste en dat die versoeking groot is om afslagtariewe vir op die nippertjie besprekings toe te staan. Die uitdaging vir die bestuur is om nuwe passasiers met afslagtariewe te lok sonder om voltariefpassasiers prys te gee. Reisklubs is een van die meganismes wat gebruik word om die voorgenoemde doelwit te bereik. Hierdie klubs bied hoofsaaklik afslagtariewe op hotelverblyf, motorhuur en vliegtuigkaartjies aan hul lede. Die aanvaarbaarheid van 'n reisklub wat beperkende rnaatreels soos, byvoorbeeld, kort kennisgewingstydperke, aanpasbare vertrek en terugkeer datums en verkorte leityd toepas, is in die Suid-Afrikaanse mark getoets. Die meerderheid van respondente in die ondersoek was ten gunste van so 'n reisklub. 'n 8elangrike bevinding is dat Suid-Afrikaners bereid is om in 'n gekose maand te reis, maar dat die spesifieke reisdatums in daardie maand aanpasbaar is in ruil vir afslagtariewe. Die navorsing toon ook dat die beskikbaarheid van fondse deurslaggewend is in die besluit om oorsee te reis of nie. Om hierdie probleem te oorkom word die instelling van 'n voorsieningsrekening aanbeveel as dee I van die reisklub se produkte. Die uitdaging aan die reisklub is dus om die vraag te konsolideer en dan af te stem op die oormaatkapasiteit van die lugrederye.

本論文分為兩部分，第一部份是探討全球航空產業的油料避險會不會對公司價值有所影響，以及油料避險的誘因。第二部份則是檢視全球航空公司的風險曝露會不會影響分析師的預測誤差，尤其是燃油價格變動的風險曝露。 / In the first essay, we examine whether jet fuel hedging increases the market value of airline companies around the world. Using a sample of 70 airline companies from 32 countries over the period 1995 to 2005, we find that jet fuel hedging is not significantly positively related to their firm value in the global airlines, but this positive relationship holds in the various sub-samples and is significant for US and non-alliance firms. Moreover, our results show that the risk-taking behavior of executives and the tendency to avoid financial distress are important determinants for the jet fuel hedging activities of non-US airline companies. Alleviating the problem of underinvestment is also an important factor to explain the jet fuel hedging activities of US and non-alliance firms. Our results add support to the growing body of literature which finds that hedging increases firm value for global airline companies. In the second essay, we examine the extent analysts revise their earnings forecasts in response to oil price, interest rate and foreign exchange rate shocks they have observed during the year, and whether these revisions contain additional information about how current and past price shocks affect reported earnings, using the sample of the global airline industry. Empirical results indicate that jet fuel hedging can increase analysts’ forecast revisions in the total sample, and in the sub-sample of the volatile fuel price period. These results can also be seen in US and non-US airlines, and airlines with both strong and weak governance. Overall, our results show that oil price shocks play an important role in investor and analyst information uncertainty with regard to the global airline industry. Consequently, corporate risk disclosures only provide limited information about firms’ financial risk exposures. Two essays are comprised in this dussertation to examine whether jet fuel hedging has effects on firm value and analysts’ forecast accuracy in the global airline industry. Using global data allows us to cmpare the differences of jet fuel hedging behavior and incentives for hedging across different sub-samples. Furthermore, we also examine how jet fuel hedging affects analysts’ forecast erros across different sub-samples and its implications for firm disclosures about their risk exposures in the financial reports. In the first essay, we examine whether jet fuel hedging increases the market value of airline companies around the world. Using a sample of 70 airline companies from 32 countries over the period 1995 to 2005, we find that jet fuel hedging is not significantly positively related to their firm value in the global airlines, but this positive relationship holds in the various sub-samples and is significant for US and non-alliance firms. Moreover, our results show that the risk-taking behavior of executives and the tendency to avoid financial distress are important determinants for the jet fuel hedging activities of non-US airline companies. Alleviating the problem of underinvestment is also an important factor to explain the jet fuel hedging activities of US and non-alliance firms. Our results add support to the growing body of literature which finds that hedging increases firm value for global airline companies. In the second essay, we examine the extent analysts revise their earnings forecasts in response to oil price, interest rate and foreign exchange rate shocks they have observed during the year, and whether these revisions contain additional information about how current and past price shocks affect reported earnings, using the sample of the global airline industry. Empirical results indicate that jet fuel hedging can increase analysts’ forecast revisions in the total sample, and in the sub-sample of the volatile fuel price period. These results can also be seen in US and non-US airlines, and airlines with both strong and weak governance. Overall, our results show that oil price shocks play an important role in investor and analyst information uncertainty with regard to the global airline industry. Consequently, corporate risk disclosures only provide limited information about firms’ financial risk exposures.