This thesis is focused on airport departure operations; its objective is to assign a value to surface surveillance information within a collaborative framework. The research develops a cooperative concept that improves the control of departure operations at busy airports and evaluates its merit using a classical and widely accepted airport departure model. The research then assumes departure operations are collaboratively controlled and develops a stochastic model of taxi operations on the airport surface. Finally, this study investigates the effect of feeding back different levels of surface surveillance information to the departure control process. More specifically, it examines the environmental and operational impact of aircraft surface location information on the taxi clearance process. Benefits are evaluated by measuring and comparing engine emissions for given runway utilization rates.

Build-Operate-Transfer (BOT) is a form of Public-Private Partnerships that is commonly used to close the growing gap between the cost of developing and modernizing transportation infrastructure systems and the financial resources available to governments. When assessing the feasibility of a BOT project, private investors consider revenue risk - which is stemmed from the uncertainty about future traffic demand - as a critical factor. A potential approach to mitigating the revenue risk is the offering of revenue risk sharing mechanisms such as Minimum Revenue Guarantee options by the government. In addition to Minimum Revenue Guarantee options, a mechanism known as Traffic Revenue Cap options may also be negotiated, which makes the government entitled to a share of revenue when it grows beyond a specified threshold. Financial valuation of investments in BOT projects should take into account uncertainty about future traffic demand, as well as Minimum Revenue Guarantee and Traffic Revenue Cap options. The conventional valuation methods including Net Present Value (NPV) analysis are not capable of integrating the uncertainty about future traffic demand in the valuation of BOT projects and properly pricing Minimum Revenue Guarantee and Traffic Revenue Cap options. Real options analysis can be used as an alternative approach to valuation of investments in transportation projects under uncertainties. However, the appropriate application of real options analysis to valuation of investments in transportation projects is conditioned upon overcoming specific theoretical challenges. Current real options models do not provide a systematic method for estimating the project volatility, which measures the variability of investment value. Existing models do not provide a method for calculating the market value of Minimum Revenue Guarantee and Traffic Revenue Cap options. Also, current models are not able to characterize the impact of Minimum Revenue Guarantee and Traffic Revenue Cap options on private investors' financial risk profile. The overarching objective of this research is to apply the real options theory in order to price Minimum Revenue Guarantee and Traffic Revenue Cap options under the uncertainty about future traffic demand. To achieve this objective, a real options model is created that characterizes the long-term traffic demand uncertainty in BOT projects and determines investors' financial risk profile under uncertainty about future traffic demand. This model presents a novel method for estimating the project volatility for real options analysis. This model devises a market-based option pricing approach to determine the correct value of Minimum Revenue Guarantee and Traffic Revenue Cap options. An appropriate procedure is created for characterizing the impact of Minimum Revenue Guarantee and Traffic Revenue Cap options on the investors' financial risk profile. The proposed real options model is applied to a BOT project to illustrate the valuation process. The limitations of the proposed real options model, as well as the barriers to its implementation, are identified and recommendations for future research are offered. This research contributes to the state of knowledge by presenting a new method for estimating the project volatility, which is required for the real options analysis of transportation investments. It also introduces a risk-neutral valuation method for pricing the market value of Minimum Revenue Guarantee and Traffic Revenue Cap options in BOT projects. The research also contributes to the state of practice by introducing a novel class of assessment tools for decision makers that characterize the investors' financial risk profile under uncertainty about future traffic demand. Proper methods for pricing of Minimum Revenue Guarantee and Traffic Revenue Cap options are useful to public and private investors, in order to avoid wasting capital in transportation projects.

Ground level ozone is one of the most ubiquitous air pollutants in urban areas, and is generated by photochemical reactions of oxides of nitrogen (NOx) and volatile organic compounds (VOCs). The effectiveness of emission reduction strategies for ozone precursors is typically evaluated using gridded, photochemical air quality models. One of the underlying assumptions in these models is that industrial emissions are nearly constant, since many industrial facilities operate continuously at a constant rate of output. However, recent studies performed in the Houston-Galveston-Brazoria area indicate that some industrial emission sources exhibit high temporal emission variability that can lead to very rapid ozone formation, especially when emissions are composed of highly reactive volatile organic compounds. This work evaluates the impact of variable emissions from industrial sources on ground-level ozone formation in Houston area, utilizing a unique hourly emission inventory, known as the 2006 Special Inventory, created as a part of the second Texas Air Quality Study. Comparison of the hourly emissions inventory data with ambient measurements indicated that the impact of the variability of industrial source emissions on ozone can be significant. Photochemical modeling predictions showed that the variability in industrial emissions can lead to differences in local ozone concentrations of as much as 27 ppb at individual ozone monitor locations. The hourly emissions inventory revealed that industrial source emissions are highly variable in nature with diverse temporal patterns and stochastic behavior. Petrochemical and chemical manufacturing flares, which represent the majority of emissions in the 2006 Special Inventory, were grouped into categories based on industrial process, chemical composition of the flared gas, and the temporal patterns of their emissions. Stochastic models were developed for each categorization of flare emissions with the goal of simulating the characterized temporal emission variability. The stochastic models provide representative temporal profiles for flares in the petrochemical manufacturing and chemical manufacturing sectors, and as such serve as more comprehensive input for photochemical air quality modeling. / text

Delivering health care services in an efficient and effective way has become a great challenge for many countries due to the aging population worldwide, rising health expenses, and increasingly complex healthcare delivery systems. It is widely recognized that models and analytical tools can aid decision-making at various levels of the healthcare delivery process, especially when decisions have to be made under uncertainty. This thesis employs stochastic models to improve decision-making under uncertainty in two specific healthcare settings: inpatient flow management and infectious disease modeling. In Part I of this thesis, we study patient flow from the emergency department (ED) to hospital inpatient wards. This line of research aims to develop insights into effective inpatient flow management to reduce the waiting time for admission to inpatient wards from the ED. Delayed admission to inpatient wards, also known as ED boarding, has been identified as a key contributor to ED overcrowding and is a big challenge for many hospitals. Part I consists of three main chapters. In Chapter 2 we present an extensive empirical study of the inpatient department at our collaborating hospital. Motivated by this empirical study, in Chapter 3 we develop a high fidelity stochastic processing network model to capture inpatient flow with a focus on the transfer process from the ED to the wards. In Chapter 4 we devise a new analytical framework, two-time-scale analysis, to predict time-dependent performance measures for some simplified versions of our proposed model. We explore both exact Markov chain analysis and diffusion approximations. Part I of the thesis makes contributions in three dimensions. First, we identify several novel features that need to be built into our proposed stochastic network model. With these features, our model is able to capture inpatient flow dynamics at hourly resolution and reproduce the empirical time-dependent performance measures, whereas traditional time-varying queueing models fail to do so. These features include unconventional non-i.i.d. (independently and identically distributed) service times, an overflow mechanism, and allocation delays. Second, our two-time-scale framework overcomes a number of challenges faced by existing analytical methods in analyzing models with these novel features. These challenges include time-varying arrivals and extremely long service times. Third, analyzing the developed stochastic network model generates a set of useful managerial insights, which allow hospital managers to (i) identify strategies to reduce the waiting time and (ii) evaluate the trade-off between the benefit of reducing ED congestion and the cost from implementing certain policies. In particular, we identify early discharge policies that can eliminate the excessively long waiting times for patients requesting beds in the morning. In Part II of the thesis, we model the spread of influenza pandemics with a focus on identifying factors that may lead to multiple waves of outbreak. This line of research aims to provide insights and guidelines to public health officials in pandemic preparedness and response. In Chapter 6 we evaluate the impact of seasonality and viral mutation on the course of an influenza pandemic. In Chapter 7 we evaluate the impact of changes in social mixing patterns, particularly mass gatherings and holiday traveling, on the disease spread. In Chapters 6 and 7 we develop agent-based simulation models to capture disease spread across both time and space, where each agent represents an individual with certain socio-demographic characteristics and mixing patterns. The important contribution of our models is that the viral transmission characteristics and social contact patterns, which determine the scale and velocity of the disease spread, are no longer static. Simulating the developed models, we study the effect of the starting season of a pandemic, timing and degree of viral mutation, and duration and scale of mass gatherings and holiday traveling on the disease spread. We identify possible scenarios under which multiple outbreaks can occur during an influenza pandemic. Our study can help public health officials and other decision-makers predict the entire course of an influenza pandemic based on emerging viral characteristics at the initial stage, determine what data to collect, foresee potential multiple waves of attack, and better prepare response plans and intervention strategies, such as postponing or cancelling public gathering events.

This dissertation consists of two self-contained studies. The first study, in the domain of stochastic inventory theory, addresses the structure of optimal ordering policies in a periodic review setting. We take multiple sources of a single product to imply an ordering cost function that is nondecreasing, piecewise linear, and convex. Our main contribution is a proof of the optimality of a finite generalized base stock policy under an average cost criterion. Our inventory model is formulated as a Markov decision process with complete observations. Orders are delivered immediately. Excess demand is fully backlogged, and the function describing holding and backlogging costs is convex. All parameters are stationary, and the random demands are independent and identically distributed across periods. The (known) distribution function is subject to mild assumptions along with the holding and backlogging cost function. Our proof uses a vanishing discount approach. We extend our results from a continuous environment to the case where demands and order quantities are integral. The second study is in the area of capacity planning. Our overarching contribution is a relatively simple and fast solution approach for the fleet composition problem faced by a retail distribution firm, focusing on the context of a major beverage distributor. Vehicles to be included in the fleet may be of multiple sizes; we assume that spot transportation capacity will be available to supplement the fleet as needed. We aim to balance the fixed costs of the fleet against exposure to high variable costs due to reliance on spot capacity. We propose a two-stage stochastic linear programming model with fixed recourse. The demand on a particular day in the planning horizon is described by the total quantity to be delivered and the total number of customers to visit. Thus, daily demand throughout the entire planning period is captured by a bivariate probability distribution. We present an algorithm that efficiently generates a "definitive" collection of bases of the recourse program, facilitating rapid computation of the expected cost of a prospective fleet and its gradient. The equivalent convex program may then be solved by a standard gradient projection algorithm.

This study is concerned with the numerical solution of certain stochastic models of chemical reactions. Such descriptions have been shown to be useful tools when studying biochemical processes inside living cells where classical deterministic rate equations fail to reproduce actual behavior. The main contribution of this thesis lies in its theoretical and practical investigation of different methods for obtaining numerical solutions to such descriptions. In a preliminary study, a simple but often quite effective approach to the moment closure problem is examined. A more advanced program is then developed for obtaining a consistent representation of the high dimensional probability density of the solution. The proposed method gains efficiency by utilizing a rapidly converging representation of certain functions defined over the semi-infinite integer lattice. Another contribution of this study, where the focus instead is on the spatially distributed case, is a suggestion for how to obtain a consistent stochastic reaction-diffusion model over an unstructured grid. Here it is also shown how to efficiently collect samples from the resulting model by making use of a hybrid method. In a final study, a time-parallel stochastic simulation algorithm is suggested and analyzed. Efficiency is here achieved by moving parts of the solution phase into the deterministic regime given that a parallel architecture is available. Necessary background material is developed in three chapters in this summary. An introductory chapter on an accessible level motivates the purpose of considering stochastic models in applied physics. In a second chapter the actual stochastic models considered are developed in a multi-faceted way. Finally, the current state-of-the-art in numerical solution methods is summarized and commented upon.

Thesis (Ph.D.) -- Mississippi State University. Department of Computer Science and Engineering. / Title from title screen. Includes bibliographical references.

Understanding the complex dynamics that underpin the response of structures in the occurrence of earthquakes is of paramount importance in ensuring community resilience. The operational continuity of structures is influenced by the performance of nonstructural components, also known as secondary structures. Inherent vulnerability characteristics, nonlinearities and uncertainties in their properties or in the excitation pose challenges that render their response determination as a non-straightforward task. This dissertation settles in the context of mathematical modelling and response quantification of seismically driven secondary systems. The case of bilinear hysteretic, rigid-plastic and free-standing rocking oscillators is first considered, as a representative class of secondary systems of distinct behaviour excited at a single point in the primary structure. The equations governing their full dynamic interaction with linear primary oscillators are derived with the purpose of assessing the appropriateness of simplified analysis methods where the secondary-primary feedback action is not accounted for. Analyses carried out in presence of pulse-type excitation have shown that the cascade approximation can be considered satisfactory for bilinear systems provided the secondary-primary mass ratio is adequately low and the system does not approach resonance. For the case of sliding and rocking systems, much lighter secondary systems need to be considered if the cascade analysis is to be adopted, with the validity of the approximation dictated by the selection of the input parameters. Based on the premise that decoupling is permitted, new analytical solutions are derived for the pulse driven nonlinear oscillators considered, conveniently expressing the seismic response as a function of the input parameters and the relative effects are quantified. An efficient numerical scheme for a general-type of excitation is also presented and is used in conjunction with an existing nonstationary stochastic far-field ground motion model to determine the seismic response spectra for the secondary oscillators at given site and earthquake characteristics. Prompted by the presence of uncertainty in the primary structure, and in line with the classical modal analysis, a novel approach for directly characterising uncertainty in the modal shapes, frequencies and damping ratios of the primary structure is proposed. A procedure is then presented for the identification of the model parameters and demonstrated with an application to linear steel frames with uncertain semi-rigid connections. It is shown that the proposed approach reduces the number of the uncertain input parameters and the size of the dynamic problem, and is thus particularly appealing for the stochastic assessment of existing structural systems, where partial modal information is available e.g. through operational modal analysis testing. Through a numerical example, the relative effect of stochasticity in a bi-directional seismic input is found to have a more prominent role on the nonlinear response of secondary oscillators when compared to the uncertainty in the primary structure. Further extending the analyses to the case of multi-attached linear secondary systems driven by deterministic seismic excitation, a convenient variant of the component-mode synthesis method is presented, whereby the primary-secondary dynamic interaction is accounted for through the modes of vibration of the two components. The problem of selecting the vibrational modes to be retained in analysis is then addressed for the case of secondary structures, which may possess numerous low frequency modes with negligible mass, and a modal correction method is adopted in view of the application for seismic analysis. The influence of various approaches to build the viscous damping matrix of the primary-secondary assembly is also investigated, and a novel technique based on modal damping superposition is proposed. Numerical applications are demonstrated through a piping secondary system multi-connected on a primary frame exhibiting various irregularities in plan and elevation, as well as a multi-connected flexible secondary system. Overall, this PhD thesis delivers new insights into the determination and understanding of the response of seismically driven secondary structures. The research is deemed to be of academic and professional engineering interest spanning several areas including seismic engineering, extreme events, structural health monitoring, risk mitigation and reliability analysis.

Cette thèse traite de quatre problèmes dans le contexte des grands systèmes distribués. Ce travail est motivé par les questions soulevées par l'expansion du Cloud Computing et des technologies associées. Le présent travail étudie l'efficacité de différents algorithmes d'allocation de ressources dans ce cadre. Les méthodes utilisées impliquent une analyse mathématique de plusieurs modèles stochastiques associés à ces réseaux. Le chapitre 1 fournit une introduction au sujet, ainsi qu'une présentation des principaux outils mathématiques utilisés dans les chapitres suivants. Le chapitre 2 présente un mécanisme de contrôle de congestion dans les services de Video on Demand fournissant des fichiers encodés dans diverses résolutions. On propose une politique selon laquelle le serveur ne livre la vidéo qu'à un débit minimal lorsque le taux d'occupation du serveur est supérieur à un certain seuil. La performance du système dans le cadre de cette politique est ensuite évaluée en fonction des taux de rejet et de dégradation. Les chapitres 3, 4 et 5 explorent les problèmes liés aux schémas de coopération entre centres de données (CD) situés à la périphérie du réseau. Dans le premier cas, on analyse une politique dans le contexte des services de cloud multi-ressources. Dans le second cas, les demandes arrivant à un CD encombré sont transmises à un CD voisin avec une probabilité donnée. Au troisième, les requêtes bloquées dans un CD sont transmises systématiquement à une autre où une politique de réservation (trunk) est introduite tel qu'une requête redirigée est acceptée seulement s'il y a un certain nombre minimum de serveurs libres dans ce CD. / This PhD thesis investigates four problems in the context of Large Distributed Systems. This work is motivated by the questions arising with the expansion of Cloud Computing and related technologies. The present work investigates the efficiency of different resource allocation algorithms in this framework. The methods used involve a mathematical analysis of several stochastic models associated to these networks. Chapter 1 provides an introduction to the subject in general, as well as a presentation of the main mathematical tools used throughout the subsequent chapters. Chapter 2 presents a congestion control mechanism in Video on Demand services delivering files encoded in various resolutions. We propose a policy under which the server delivers the video only at minimal bit rate when the occupancy rate of the server is above a certain threshold. The performance of the system under this policy is then evaluated based on both the rejection and degradation rates. Chapters 3, 4 and 5 explore problems related to cooperation schemes between data centres on the edge of the network. In the first setting, we analyse a policy in the context of multi-resource cloud services. In second case, requests that arrive at a congested data centre are forwarded to a neighbouring data centre with some given probability. In the third case, requests blocked at one data centre are forwarded systematically to another where a trunk reservation policy is introduced such that a redirected request is accepted only if there are a certain minimum number of free servers at this data centre.

Made available in DSpace on 2014-06-11T19:25:32Z (GMT). No. of bitstreams: 0 Previous issue date: 2003-02-26Bitstream added on 2014-06-13T19:53:23Z : No. of bitstreams: 1 diniz_ec_me_rcla.pdf: 610866 bytes, checksum: 86834ae8acca4f4532e7d39107c9c8c7 (MD5) / Modelos de geração de precipitações são de extrema importância nos dias atuais, pois com o conhecimento do padrão de precipitação em certa área, pode-se planejar obras de forma a minimizar os efeitos das precipitações de grande intensidade. No presente trabalho, aplica-se o modelo de Neyman-Scott e, particularmente, o de Poisson na geração de precipitações de grande intensidade na região da Bacia do Tietê Superior, no Estado de São Paulo, Brasil. Essa região sofre anualmente com as enchentes devido às fortes precipitações e a alta densidade populacional nesta área. Para a aplicação dos modelos de distribuição espacial de precipitações Neyman-Scott e Poisson, foram considerados os dados coletados de 1980 a 1997 de uma rede pluviométrica constituída de treze pluviômetros. / Models related with precipitations generation have extremely importance nowadays because with the standard knowledge about an specific area, we can plan projects to minimize the effects caused by high intensity precipitations. At the present work, we applies Neyman-Scott s model and particularly the one from Poisson, in the precipitations generations with high intensity in the Superior Tietê Bays region, São Paulo state, Brazil. This region suffer annually with the floods due to the strong precipitations and the high human density. To use the Neyman-Scott and Poisson models related to spatial precipitations distribution, we have considered data collected during 1980 to 1997 from a pluviometric network consisted by thirteen rain gauges.