by Lau Tze Yiu, Peter. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (p. 352-365). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web.

In this thesis we consider two comparative statics questions of changes in risk. The first question concerns situations where an individual faces some risk and has no control over the uncertain environment. In these situations we ask what kind of changes in risk will cause the individual's expected utility to increase. The second comparative statics question concerns situations where an individual faces some risk and has some control over the uncertain environment. In particular, we consider situations where the individual maximizes her expected utility with respect to some control parameter. Here we ask what kind of changes in risk will cause the individual's optimal value of the control parameter to increase. The existing literature has answered these questions for a class of individuals (for example, the class of risk averse individuals). This thesis differs from existing literature as it focuses on a given individual, and thus reveals some of the person-specific factors that affect individual?s responses to changes in risk. The aim of the thesis is to show how an order on distributions, termed single crossing likelihood ratio (SCLR) order, can intuitively answer both questions for a given individual. The main contributions of the thesis are as follows. First, the thesis presents the SCLR order and its main properties. Second, the thesis shows that the SCLR order can answer the above comparative statics questions in an intuitive way. In particular, the thesis shows that the answer to the above questions, with the use of the SCLR order, depends on a risk reference point which can be interpreted as a "certainty equivalent" point. Thus it is demonstrated that individual's responses to changes in risk are affected by her "certainty equivalent" point. Lastly, the results of the thesis can be used to provide an intuitive explanation of related existing results that were obtained for a class of individuals.

The surveillance of geographic borders and critical infrastructures using limited sensor capability has always been a challenging task in many homeland security applications. While geographic borders may be very long and may go through isolated areas, critical assets may be large and numerous and may be located in highly populated areas. As a result, it is virtually impossible to secure each and every mile of border around the country, and each and every critical infrastructure inside the country. Most often, a compromise must be made between the percentage of border or critical asset covered by surveillance systems and the induced cost. Although threats to homeland security can be conceived to take place in many forms, those regarding illegal penetration of the air, land, and maritime domains under the cover of day-to-day activities have been identified to be of particular interest. For instance, the proliferation of drug smuggling, illegal immigration, international organized crime, resource exploitation, and more recently, modern piracy, require the strengthening of land border and maritime awareness and increasingly complex and challenging national security environments. The complexity and challenges associated to the above mission and to the protection of the homeland may explain why a methodology enabling the design and optimization of distributed detection systems architectures, able to provide accurate scanning of the air, land, and maritime domains, in a specific geographic and climatic environment, is a capital concern for the defense and protection community. This thesis proposes a methodology aimed at addressing the aforementioned gaps and challenges. The methodology particularly reformulates the problem in clear terms so as to facilitate the subsequent modeling and simulation of potential operational scenarios. The needs and challenges involved in the proposed study are investigated and a detailed description of a multidisciplinary strategy for the design and optimization of detection architectures in terms of detection performance and cost is provided. This implies the creation of a framework for the modeling and simulation of notional scenarios, as well as the development of improved methods for accurate optimization of detection architectures. More precisely, the present thesis describes a new approach to determining detection architectures able to provide effective coverage of a given geographical environment at a minimum cost, by optimizing the appropriate number, types, and locations of surveillance and detection systems. The objective of the optimization is twofold. First, given the topography of the terrain under study, several promising locations are determined for each sensor system based on the percentage of terrain it is covering. Second, architectures of sensor systems able to effectively cover large percentages of the terrain at minimal costs are determined by optimizing the number, types and locations of each detection system in the architecture. To do so, a modified Genetic Algorithm and a modified Particle Swarm Optimization are investigated and their ability to provide consistent results is compared. Ultimately, the modified Particle Swarm Optimization algorithm is used to obtain a Pareto frontier of detection architectures able to satisfy varying customer preferences on coverage performance and related cost.

Fractional aircraft ownership programs offer companies and individuals all the benefits of owning private jet, such as safety, consistency, and guaranteed availability, at a fraction of the cost of owning an aircraft. In the fractional ownership model, the partial owners of an aircraft are entitled to certain number of hours per year, and the management company is responsible for all the operational considerations and making sure an aircraft is available to the owners at the requested time and location. This thesis research proposes advance optimization techniques to help the management company to optimally operate its available resources and provides tools for strategic decision making. The contributions of this thesis are: (i) The development of optimization methodologies to assign and schedule aircraft and crews so that all flight requests are covered at the lowest possible cost. First, a simple model is developed to solve the crew pairing and aircraft routing problem with column generation assuming that a crew stays with one specific aircraft during its duty period. Secondly, this assumption is partially relaxed to improve resource utilization by revising the simple model to allow a crew to use another aircraft when its original aircraft goes under long maintenance. Thirdly, a new comprehensive model utilizing Benders decomposition technique and a fleet-station time line is proposed to completely relax the assumption that crew stays with one specific aircraft. It combines the fleet assignment, aircraft routing, and crew pairing problems. In the proposed methodologies, real world details are taken into consideration, such as crew transportation and overtime costs, scheduled and unscheduled maintenance effects, crew rules, and the presence of non-crew-compatible fleets. Scheduling with time windows is also discussed. (ii) The analysis of operational strategies to provide decision making support. Scenario analyses are performed to provide insights on improving business profitability and aircraft availability, such as impact of aircraft maintenance, crew swapping, effect of increasing demand by Jet-card and geographical business expansion, size of company owned aircraft, and strategies to deal with the stochastic feature of unscheduled maintenance and demand.

D. Tech. Industrial Engineering. / Discusses purpose of this study is to derive a new model capable of advanced diagnosis and analysis of energy usage to determine the possible energy efficiency potential through the following in a single model: Analysis of industrial historical data; Prediction of the industrial energy baseline; Computation of the industrial energy efficiency; and Optimization of the industrial energy consumption usage. In this context, the development of a new model involves: Carrying out literature survey; Carrying out Mathematical Analysis of the dynamics of energy efficiency in an industry; Critically analyzing and testing existing models; Evolve a new and novel model; Test the model using data from specific industry; Apply the model to eleven industrial sectors in South Africa. This thesis on energy efficiency potential will be a milestone for different stakeholders, policymakers and decision makers in the energy sector at national and international levels who are, or will be interested in reducing energy input and still produce the observed output levels, by becoming technically efficient. The approach adopted by the study is the integration of Index Decomposition Analysis (IDA), Artificial Neural Network (ANN) and Data Envelopment Analysis (DEA) into a single model.This methodology combines modeling, which is at the core of an energy-management technique, with a wider interpretation of activity growth, structure and efficiency changes which contribute to changes in energy consumption.

This dissertation describes the development of a requirements analysis methodology that takes into account the concept of operations and the hierarchical decomposition of aerospace systems. At the core of the methodology, the Analytic Network Process (ANP) is used to ensure the traceability between the qualitative and quantitative information present in the hierarchical model. The proposed methodology is implemented to the requirements definition of a hurricane tracker Unmanned Aerial Vehicle. Three research objectives are identified in this work; (1) improve the requirements mapping process by matching the stakeholder expectations with the concept of operations, systems and available resources; (2) reduce the epistemic uncertainty surrounding the requirements and requirements mapping; and (3) improve the requirements down-selection process by taking into account the level of importance of the criteria and the available resources. Several challenges are associated with the identification and definition of requirements. The complexity of the system implies that a large number of requirements are needed to define the systems. These requirements are defined early in the conceptual design, where the level of knowledge is relatively low and the level of uncertainty is large. The proposed methodology intends to increase the level of knowledge and reduce the level of uncertainty by guiding the design team through a structured process. To address these challenges, a new methodology is created to flow-down the requirements from the stakeholder expectations to the systems alternatives. A taxonomy of requirements is created to classify the information gathered during the problem definition. Subsequently, the operational and systems functions and measures of effectiveness are integrated to a hierarchical model to allow the traceability of the information. Monte Carlo methods are used to evaluate the variations of the hierarchical model elements and consequently reduce the epistemic uncertainty. The proposed methodology is applied to the design of a hurricane tracker Unmanned Aerial Vehicles to demonstrate the origin and impact of requirements on the concept of operations and systems alternatives. This research demonstrates that the hierarchical modeling methodology provides a traceable flow-down of the requirements from the problem definition to the systems alternatives phases of conceptual design.

In this thesis we consider two comparative statics questions of changes in risk. The first question concerns situations where an individual faces some risk and has no control over the uncertain environment. In these situations we ask what kind of changes in risk will cause the individual's expected utility to increase. The second comparative statics question concerns situations where an individual faces some risk and has some control over the uncertain environment. In particular, we consider situations where the individual maximizes her expected utility with respect to some control parameter. Here we ask what kind of changes in risk will cause the individual's optimal value of the control parameter to increase. The existing literature has answered these questions for a class of individuals (for example, the class of risk averse individuals). This thesis differs from existing literature as it focuses on a given individual, and thus reveals some of the person-specific factors that affect individual?s responses to changes in risk. The aim of the thesis is to show how an order on distributions, termed single crossing likelihood ratio (SCLR) order, can intuitively answer both questions for a given individual. The main contributions of the thesis are as follows. First, the thesis presents the SCLR order and its main properties. Second, the thesis shows that the SCLR order can answer the above comparative statics questions in an intuitive way. In particular, the thesis shows that the answer to the above questions, with the use of the SCLR order, depends on a risk reference point which can be interpreted as a "certainty equivalent" point. Thus it is demonstrated that individual's responses to changes in risk are affected by her "certainty equivalent" point. Lastly, the results of the thesis can be used to provide an intuitive explanation of related existing results that were obtained for a class of individuals.

Thesis (MEng)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: The management of physical assets has become a popular eld of study over recent years and is being acknowledged in multiple disciplines world wide. In this project, research on Physical Asset Management (PAM), maintenance and decision making are presented. PAM is a complex subject and requires the participation of multiple disciplines in order to successfully manage physical assets. Moreover, the management of maintenance makes a big contribution in achieving successful PAM. Decision making is a core element to manage maintenance e ciently, both on strategic and operational level. Various methods and techniques can be used to aid the decision making process such as, using past experience, xed decision making techniques and techniques involving numerical calculations, to mention only a few. However, using numerical calculations to make decisions are not very popular. This is due to various reasons, for example the inherent complexity of the mathematics and the time required to execute such calculations are disliked. People tend to avoid complex numerical calculations and rather rely on past experience and discussion of circulating opinions to make decisions. This is not ideal and can lead to inconsistent and inaccurate decisions. In this project, the importance of numerical decision making is researched, especially in maintenance related decisions. The focus is placed on the simpli cation of numerical decision making techniques with the aim to make it easy and quick to use to support operational PAM decisions. Di erent decisions regarding PAM, especially decisions with regards to managing maintenance in order to achieve PAM, are discussed by means of a literature study. This is done to clarify the applicability of using numerical decision making techniques to support this type of decisions. A few di erent available numerical techniques are highlighted that can be used to support the decision making process. The decisions together with numerical decision making techniques are evaluated in order to combine the most appropriate techniques in a simpli ed manner. The purpose of this is that it can be used by anyone with the necessary knowledge of a speci c system or operation. As a result a simpli ed numerical decision making toolbox is developed that can support maintenance related decision. This toolbox is applied to a real life situation by means of a case study, made possible by Anglo American Platinum Limited (Amplats). An evaluation and validation of the toolbox is done through the case study to conclude wether it has value in practice or not. / AFRIKAANSE OPSOMMING: Die bestuur van siese bates het die afgelope paar jaar 'n gewilde studieveld geword en word erken in verskeie dissiplines reg oor die w^ereld. In hierdie projek word navorsing gedoen oor Fisiese Bate Bestuur (FBB), instandhouding en besluitneming. FBB is 'n komplekse onderwerp en vereis die deelname van verskeie dissiplines om sukses te behaal. Die bestuur van instandhouding maak 'n groot bydrae tot suksesvolle FBB. 'n Kern element van doeltre ende instandhouding is besluitneming, beide op strategiese en operasionele vlak. Verskillende metodes en tegnieke kan gebruik word om die besluitnemingsproses te ondersteun soos byvoorbeeld om gebruik te maak van ondervinding en vorige gebeurtenisse, vaste besluitnemingstegnieke, tegnieke wat numeriese berekeninge gebruik en nog meer. Die gebruik van numeriese metodes om besluite te neem is nie baie gewild nie. Dit is as gevolg van verskeie redes soos byvoorbeeld die inherente kompleksiteit en ingewikkeldheid van die wiskunde en ook die tyd wat benodig word om sulke berekeninge uit te voer. Mense is geneig om ingewikkelde numeriese berekeninge te vermy en eerder staat te maak op vorige ervaring en die bespreking van menings om besluite te neem. Dit is nie ideaal nie en kan lei tot onkonsekwente besluite, of selfs verkeerde besluite. In hierdie projek is die belangrikheid van numeriese besluitneming nagevors, veral in die onderhoudsverwante besluite. Die fokus word geplaas op die vereenvoudiging van die numeriese besluitnemings tegnieke. Die doel is om dit op so 'n manier te vereenvoudig dat dit maklik en vinnig is om te gebruik vir operasionele FBB besluite. Verskillende besluite oor FBB, veral besluite met betrekking tot instandhouding om suksesvolle FBB te bereik, word bespreek deur middel van 'n literatuurstudie. Die literatuurstudie ondersoek die toepaslikheid van die gebruik van numeriese besluitnemingstegnieke vir hierdie soort besluite. 'n Paar verskillende beskikbare numeriese tegnieke wat gebruik kan word om die besluitnemingsproses te ondersteun word uitgelig. Die besluite, saam met numeriese besluitnemingtegnieke, word ge evalueer om die mees gepaste tegnieke te kombineer in 'n vereenvoudigde manier. Uiteindelik moet dit deur enige iemand met die nodige kennis van 'n spesi eke stelsel of proses gebruik kan word. As resultaat is 'n vereenvoudigde numeriese besluitnemingstegniekkombinasie ontwikkel wat besluite verwant aan instandhouding kan ondersteun. Hierdie tegniek-kombinasie word toegepas in 'n werklike situasie deur middel van 'n gevallestudie, wat moontlik gemaak is deur Anglo American Platinum Limited. 'n Evaluering en validering van die tegniek-kombinasie word gedoen in die gevallestudie om te bepaal of dit wel waarde het in die praktyk of nie.

This research provides performance metrics for cooperative research centers that enhance translational research formed by the partnership of government, industry and academia. Centers are part of complex ecosystems that vary greatly in the type of science conducted, organizational structures and expected outcomes. The ability to realize their objectives depends on transparent measurement systems to assist in decision making in research translation. A generalizable, hierarchical decision model that uses both quantitative and qualitative metrics is developed based upon program goals. Mission-oriented metrics are used to compare the effectiveness of the cooperative research centers through case studies. The US National Science Foundation (NSF) industry university cooperative research center (IUCRC) program is the domain of organizational effectiveness because of its longevity, clear organizational structure, repeated use and availability of data. Not unlike a franchise business model, the program has been replicated numerous times gaining recognition as one of the most successful federally funded collaborative research center (CRC) programs. Understanding IUCRCs is important because they are a key US policy lever for enhancing translational research. While the program model is somewhat unique, the research project begins to close the gap for comparing CRCs by introducing a generalizable model and method into the literature stream. Through a literature review, program objectives, goals, and outputs are linked together to construct a four-level hierarchical decision model (HDM). A structured model development process shows how experts validate the content and construct of the model using these linked concepts. A subjective data collection approach is discussed showing how collection, analysis and quantification of expert pair-wise-comparison data is used to establish weights for each of the decision criteria. Several methods are discussed showing how inconsistency and disagreement are measured and analyzed until acceptable levels are reached. Six case studies are used to compare results, evaluate the impact of expert disagreement and conduct criterion-related validity. Comparative analysis demonstrates the ability of the model to efficiently ascertain criteria that are relatively more important towards each center's performance score. Applying this information, specific performance improvement recommendations for each center are presented. Upon review, experts generally agreed with the results. Criterion-related validity discusses how the performance measurement scoring system can be used for comparative analysis among science and engineering focused research centers. Dendrograms highlight where experts disagree and provide a method for further disagreement analysis. Judgment quantification values for different expert clusters are substituted into the model one-at-a-time (OAT) providing a method to analyze how changes in decisions based on these disagreements impact the results of the model's output. This research project contributes to the field by introducing a generalizable model and measurement system that compares performance of NSF supported science and engineering focused research centers.

Indiana University-Purdue University Indianapolis (IUPUI) / Domain-specific models powered using domain-specific modeling languages are traditionally created manually by modelers. There exist model intelligence techniques, such as constraint solvers and model guidance, which alleviate challenges associated with manually creating models, however parts of the modeling process are still manual. Moreover, state-of-the-art model intelligence techniques are---in essence---reactive (i.e., invoked by the modeler). This thesis therefore provides two contributions to model-driven engineering research using domain-specific modeling language (DSML). First, it discusses how DSML semantic and constraint can enable proactive modeling, which is a form of model intelligence that foresees model transformations, automatically executes these model transformations, and prompts the modeler for assistance when necessary. Secondly, this thesis shows how we integrated proactive modeling into the Generic Modeling environment (GME). Our experience using proactive modeling shows that it can reduce modeling effort by both automatically generating required model elements, and by guiding modelers to select what actions should be executed on the model.