Proposed in this thesis is a methodology to enable yacht designers to develop innovative structural concepts, even when the loads experienced by the yacht are highly uncertain, and has been implemented in sufficient detail to confirm the feasibility of this new approach. The new approach is required because today??s yachts are generally lighter, getting larger and going faster. The question arises as to how far the design envelope can be pushed with the highly uncertain loads experienced by the structure? What are the effects of this uncertainty and what trade-offs in the structural design will best meet the overall design objectives? The new approach provides yacht designers with a means of developing innovative structural solutions that accommodate high levels of uncertainty, but still focus on best meeting design objectives constrained by trade-offs in weight, safety and cost. The designer??s preferences have a large, and not always intuitive, influence on the necessary design trade-offs. This in turn invites research into ways to formally integrate decision algorithms into knowledge-based design systems. A lean and robust design system has been achieved by developing a set of tools which are blanketed by a fuzzy decision algorithm. The underlying tool set includes costing, material optimisation and safety analysis. Central to this is the innovative way in which the system allows non-discrete variables to be utilized along with new subjective measures of structural reliability based on load path algorithms and topological (shape) optimisation. The originality in this work is the development of a knowledge-based framework and methodology that uses a fuzzy decision making tool to navigate through a design space and address trade-offs between high level objectives when faced with limited design detail and uncertainty. In so doing, this work introduces the use of topological optimisation and load path theory to the structural design of yachts as a means of overcoming the historical focus of knowledge-based systems and to ensure that innovative solutions can still evolve. A sensitivity analysis is also presented which can quantify a design??s robustness in a system that focuses on a global approach to the measurement of objectives such as cost, weight and safety. Results from the application of this system show new and innovative structural solutions evolving that take into account the designers preferences regarding cost, weight and safety while accommodating uncertain parameters such as the loading experienced by the hull.

A fusion system sometimes requires the capability to represent the temporal changes of uncertain sensory information in dynamic and uncertain situation. A Bayesian Network can construct a coherent fusion structure with the hypothesis node which cannot be observed directly and sensors through a number of intermediate nodes that are interrelated by cause and effect. In some BN applications for observing a hypothesis node with the number of participated sensors, rank and select the appropriate options (different combination of sensors allocation) in the decision-making is a challenging problem. By user interaction, we can acquire more and useful information through multi-criteria decision aid (MCDA) as semi-automatically decision support. So in this study, Multi Attribute Decision Making (MADM) techniques as TOPSIS, SAW, and Mixed (Rank Average) for decision-making as well as AHP and Entropy for obtaining the weights of indexes have been used. Since MADM techniques have most probably different results according to different approaches and assumptions in the same problem, statistical analysis done on them. According to results, the correlation between applied techniques for ranking BN options is strong and positive because of the close proximity of weights suggested by AHP and Entropy. Mixed method as compared to TOPSIS and SAW is ideal techniques; moreover, AHP is more acceptable than Entropy for weighting of indexes.

Doctor of Philosophy / Department of Industrial and Manufacturing Systems Engineering / Bradley Kramer / This research shows that industry and company related performance indicators enhance the business valuation process by providing a broader, more encompassing view of overall corporate health and a better understanding of improvement opportunity areas within a company. To incorporate performance indicators in the business valuation process, new methodologies are required to integrate the non-financial and soft data with the typical financial information used in business valuation. This requires a “re-think” of the standard business valuation process and the exploration and application of other methods and analytical techniques. The results of this research are the definition of a problem type and the development of a new business valuation process. The problem structure has as inputs industry specific performance metrics grouped into three primary areas Production Processes, Products/Services and Marketability and Management, a fuzzy logic model with fuzzy and approximate relationships between performance metrics and financial information and crisp financial information as output. The framework for a fuzzy logic model was developed and is used to approximate relationships and model a non-linear environment. The resulting crisp financial information is then input and integrated into the traditional business valuation process. The process was demonstrated with an example production company and with data from two regional airlines. A step-by-step example of the process was provided using the production company example to demonstrate how the results are generated and integrated with DCF business valuation. Heuristics to identify areas to improve company performance were described. Two regional airlines, individually and combined, were tested with actual data using the original fuzzy logic model structure and then the original fuzzy logic model structure was revised and new results generated. Tuning the model showed an improvement in the business valuation process performance. The benefits from this research include the definition of a new class of problems and a process to solve problems of this nature. The insights gained from this research can be applied in major disciplines such as accounting, business and finance, engineering and decision theory.

Il existe deux approches classiques et bien comprises des tâches de traitement vidéo pour la vidéoconférence. Le premier utilise une unité centralisée de contrôle multipoint. Dans la deuxième approche, les tâches de traitement vidéo sont directement traitées dans les clients. La performance est ensuite limitée par les caractéristiques du périphérique. Dans cette thèse, nous proposons une troisième approche alternative. Nous proposons un système qui distribue des tâches de traitement vidéo en temps réel sur les ressources internes de l'entreprise. Une méthode dédiée de prise de décision basée sur les multi-attributs est conçue pour tenir compte de la variété des attributs qui influent sur la qualité de l'expérience. Des algorithmes de distribution et de redistribution de tâches sont élaborés. Nous testons ensuite l'approche proposée au moyen de la simulation afin d'étudier l'impact des principaux paramètres critiques. L'approche proposée pose une question sur laquelle un PC peut être utilisé comme plate-forme pour le serveur multimédia. Nous estimons une qualité perçue des flux vidéo afin d'étudier l'influence de la charge CPU. Nous avons également élaboré des algorithmes, combinant l'approche Cloud / Fog avec différents types de serveurs multimédia. Le résultat fournit une solution de conférence optimisée en termes de coût tant pour le fournisseur que pour le consommateur, ainsi que pour l'expérience de l'utilisateur final. En combinant les algorithmes élaborés et l'architecture avec les résultats de l'expérimentation, nous concluons que la solution proposée peut être utilisée comme une nouvelle approche de la problématique de la vidéoconférence. / There exist two classical and well-understood approaches to video processing tasks for videoconferencing. The first one is using a centralized Multipoint Control Unit (MCU). In the second approach, the video processing tasks are directly handled in endpoints. Performance is then restricted by device characteristics, especially in the case of mobile devices. In this thesis, we propose a third alternative approach. We propose a system, which distributes real-time video processing tasks on enterprise desktop grid. A dedicated Multi Attribute Decision Making method is designed in order to take into account the variety of attributes impacting Quality of Experience. A number of task distribution and redistribution algorithms are elaborated. We then test the proposed approach by means of simulation in order to study the impact of the main critical parameters. The proposed approach arises a question to which extent a PC can be used as a platform for media server and how CPU load affects the quality of provided video conference. We estimate a perceived quality of video streams in order to investigate CPU load influence. Also we elaborated algorithms, combining Cloud/Fog approach with different types of media servers, the result provides an optimized conferencing solution in the terms of cost for both provider and consumer as well as in terms of end user experience. Combining elaborated algorithms and architecture with experimentation results we conclude that proposed solution can be used as a novel approach to video conferencing problematic.

The past decades have seen the state of the art in aerospace system design progress from a scope of simple optimization to one including robustness, with the objective of permitting a single system to perform well even in off-nominal future environments. Integrating flexibility, or the capability to easily modify a system after it has been fielded in response to changing environments, into system design represents a further step forward. One challenge in accomplishing this rests in that the decision-maker must consider not only the present system design decision, but also sequential future design and operation decisions. Despite extensive interest in the topic, the state of the art in designing flexibility into aerospace systems, and particularly space systems, tends to be limited to analyses that are qualitative, deterministic, single-objective, and/or limited to consider a single future time period. To address these gaps, this thesis develops a stochastic, multi-objective, and multi-period framework for integrating flexibility into space system design decisions. Central to the framework are five steps. First, system configuration options are identified and costs of switching from one configuration to another are compiled into a cost transition matrix. Second, probabilities that demand on the system will transition from one mission to another are compiled into a mission demand Markov chain. Third, one performance matrix for each design objective is populated to describe how well the identified system configurations perform in each of the identified mission demand environments. The fourth step employs multi-period decision analysis techniques, including Markov decision processes (MDPs) from the field of operations research, to find efficient paths and policies a decision-maker may follow. The final step examines the implications of these paths and policies for the primary goal of informing initial system selection. Overall, this thesis unifies state-centric concepts of flexibility from economics and engineering literature with sequential decision-making techniques from operations research. The end objective of this thesis' framework and its supporting analytic and computational tools is to enable selection of the next-generation space systems today, tailored to decision-maker budget and performance preferences, that will be best able to adapt and perform in a future of changing environments and requirements. Following extensive theoretical development, the framework and its steps are applied to space system planning problems of (1) DARPA-motivated multiple- or distributed-payload satellite selection and (2) NASA human space exploration architecture selection.

The problems, associated with sensitivity of quantitative multiattribute decision-making methods to the initial data and the reliability of the decision obtained by using these methods, are considered in the presented dissertaition. The methods discussed are applied to solving decision-making problems, consisting in the selection of one alternative out of the available several options, when a decision-making person seeks to achieve a number of aims, rather than a single aim, and the alternatives are evaluated based on a set of attribute, which may be conflicting. The research object is sensitivity of the popular quantitative multiattribute decision-making methods: TOPSIS, SAW and COPRAS to the initial data errors. The initial data inaccuracy may be caused by the errors made by a person, entering the data. The estimates of the alternatives may have some errors, which are not taken into account, when the considered methods are used with these data. The analysis of the related works has shown that there are methods for evaluating sensitivity with respect to one attribute, based on varying the significance values, such as the method of the most critical criterion (Triantaphyllou 2000) and to many attributes - Tornado diagram. However, these methods do not show either the errors of solution results or the reliability level of the result obtained. The main aim of the dissertation is the evaluation of the stochastic nature of multiattribute decision-making methods, based on... [to full text] / Disertacijoje nagrinėjamas kiekybinių daugiatikslių sprendimo priėmimo metodų jautrumas ir juos taikant gautų sprendimų patikimumas. Daugiatiksliai sprendimo priėmimo metodai taikomi sprendimo priėmimo uždaviniuose, renkantis vieną alternatyvą iš keleto galimų, kai sprendimą priimantis asmuo siekia ne vieno, o kelių tikslų, o alternatyvos vertinamos pagal daugelį efektyvumo rodiklių, kurie gali būti tarpusavyje prieštaraujantys (pvz. kainos ir naudos rodikliai). Pagrindinis tyrimo objektas yra metodų TOPSIS, SAW ir COPRAS jautrumas pradinių duomenų netikslumams. Duomenų netikslumai galimi dėl pradinių duomenų reikšmes nustatančio asmens padarytų klaidų. Tokiu atveju, kai taikant minėtus metodus, alternatyvų vertinimui naudojami pradiniais duomenys turintys tam tikras paklaidas, alternatyvų vertinimai gali būti su paklaidomis, kurios, taikant šiuos metodus, nebėra vertinamos. Išanalizavus susijusius darbus, nustatyta, jog yra metodų, skirtų vertinti sprendimo jautrumą vieno rodiklio atžvilgiu, keičiant reikšmingumo reikšmes, tai - kritiškiausio kriterijaus metodas (Triantaphyllou 2000) ir daugelio rodiklių atžvilgiu - Tornado diagrama. Tačiau šie metodai nepateikia nei sprendimo rezultatų paklaidų, nei rezultato patikimumo laipsnio. Pagrindinis disertacijos tikslas – įvertinti daugiatikslių, kiekybiniais matavimais pagrįstų sprendimo priėmimo metodų stochastiką. Sukurtų metodų taikymo sritis – sprendimo paramos sistemų projektavimas, sprendimų patikimumo ir sprendimų priėmimo... [toliau žr. visą tekstą]