Kyoto University (京都大学) / 0048 / 新制・課程博士 / 博士(工学) / 甲第14577号 / 工博第3045号 / 新制||工||1453(附属図書館) / 26929 / UT51-2009-D289 / 京都大学大学院工学研究科航空宇宙工学専攻 / (主査)教授 吉村 允孝, 教授 椹木 哲夫, 教授 松原 厚 / 学位規則第4条第1項該当

Redundancy Allocation Problem (RAP) is a well known mathematical problem for modeling series-parallel systems. It is a combinatorial optimization problem which focuses on determining an optimal assignment of components in a system design. Due to the diverse possible selection of components, the RAP is proved to be NP-hard. Therefore, many algorithms, especially heuristic algorithms were proposed and implemented in the past several decades, committed to provide innovative methods or better solutions. In recent years, multi-agent system (MAS) is proposed for modeling complex systems and solving large scale problems. It is a relatively new programming concept with the ability of self-organizing, self-adaptive, autonomous administrating, etc. These features of MAS inspire us to look at the RAP from another point of view. An RAP can be divided into multiple smaller problems that are solved by multiple agents. The agents can collaboratively solve optimal RAP solutions quickly and efficiently. In this research, we proposed to solve RAP using MAS. This novel approach, to the best of our knowledge, has not been proposed before, although multi-agent approaches have been widely used for solving other large and complex nonlinear problems. To demonstrate that, we analyzed and evaluated four benchmark RAP problems in the literature. From the results, the MAS approach is shown as an effective and extendable method for solving the RAP problems. / Electrical and Computer Engineering

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal.

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Maintaining and operating manual main distribution frames is labour-intensive. As a result, Automated Main Distribution Frames (AMDFs) have been developed to alleviate the task of maintaining subscriber loops. Commercial AMDFs are currently employed in telephone exchanges in some parts of the world. However, the most significant factors limiting their widespread adoption are costeffective scalability and reliability. Therefore, an impelling incentive is provided to create a simulation framework in order to explore typical implementations and scenarios. Such a framework will allow the evaluation and optimisation of a design in terms of both internal and external redundancies. One of the approaches to improve system performance, such as system reliability, is to allocate the optimal redundancy to all or some components in a system. Redundancy at the system or component levels can be implemented in one of two schemes: parallel redundancy or standby redundancy. It is also possible to mix these schemes for various components. Moreover, the redundant elements may or may not be of the same type. If all the redundant elements are of different types, the redundancy optimisation model is implemented with component mixing. Conversely, if all the redundant components are identical, the model is implemented without component mixing. The developed framework can be used both to develop new AMDF architectures and to evaluate existing AMDF architectures in terms of expected lifetimes, reliability and service availability. Two simulation models are presented. The first simulation model is concerned with optimising central office equipment within a telephone exchange and entails an environment of clients utilising services. Currently, such a model does not exist. The second model is a mathematical model incorporating stochastic simulation and a hybrid intelligent evolutionary algorithm to solve redundancy allocation problems. For the first model, the optimal partitioning of the model is determined to speed up the simulation run efficiently. For the second model, the hybrid intelligent algorithm is used to solve the redundancy allocation problem under various constraints. Finally, a candidate concept design of an AMDF is presented and evaluated with both simulation models. / Dissertation (MEng)--University of Pretoria, 2013. / gm2014 / Electrical, Electronic and Computer Engineering / unrestricted

Embedded Systems may have competing design objectives, such as to maximize the reliability, increase the functional safety, minimize the product cost, and minimize the energy consumption. The architectures must be therefore configured to meet varied requirements and multiple design objectives. In particular, reliability and safety are receiving increasing attention. Consequently, the configuration of fault-tolerant mechanisms is a critical design decision. This work proposes a method for automatic selection of appropriate fault-tolerant design patterns, optimizing simultaneously multiple objective functions. Firstly, we present an exact method that leverages the power of Satisfiability Modulo Theory to encode the problem with a symbolic technique. It is based on a novel assessment of reliability which is part of the evaluation of alternative designs. Afterwards, we empirically evaluate the performance of a near-optimal approximation variation that allows us to solve the problem even when the instance size makes it intractable in terms of computing resources. The efficiency and scalability of this method is validated with a series of experiments of different sizes and characteristics, and by comparing it with existing methods on a test problem that is widely used in the reliability optimization literature.

Submitted by Daniella Sodre (daniella.sodre@ufpe.br) on 2015-04-10T16:15:19Z No. of bitstreams: 2 dscidl.pdf: 3672005 bytes, checksum: 16e2ea719e96351a648acbff70be2fb0 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Made available in DSpace on 2015-04-10T16:15:19Z (GMT). No. of bitstreams: 2 dscidl.pdf: 3672005 bytes, checksum: 16e2ea719e96351a648acbff70be2fb0 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2013-02-27 / CNPq / Nesse trabalho são desenvolvidos modelos de quantificação de métricas de risco e confiabilidade para sistemas em diferentes etapas do ciclo de vida. Para sistemas na fase de projeto, um Algoritmo Genético Multiobjetivo (MOGA) é combinado à Simulação Discreta de Eventos (DES) a fim de prover configurações não-dominadas com relação à disponibilidade e ao custo. O MOGA + DES proposto incorpora Processos de Renovação Generalizados para modelagem de reparos imperfeitos e também indica o número ótimo de equipes de manutenção. Para a fase operacional é proposto um hibridismo entre MOGA e Inspeção Baseada no Risco para elaboração de planos de inspeção não-dominados em termos de risco e custo que atendem às normas locais. Regressão via Support Vector Machines (SVR) é aplicada nos casos em que a métrica relacionada à confiabilidade (variável resposta) de um sistema operacional é função de variáveis ambientais e operacionais com expressão analítica desconhecida. Otimização via Nuvens de Partículas é combinada à SVR para a seleção simultânea das variáveis explicativas mais relevantes e dos valores dos hiperparâmetros que aparecem no problema de treinamento de SVR. Com o objetivo de avaliar a incerteza relacionada à variável resposta, métodos bootstrap são combinados à SVR para a obtenção de intervalos de confiança e de previsão. São realizados experimentos numéricos e são apresentados exemplos de aplicação no contexto da indústria do petróleo. Os resultados obtidos indicam que os modelos propostos fornecem informações importantes para o planejamento de custos e para a implementação de ações apropriadas a fim de evitar eventos indesejados. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------This work develops models for quantifying risk and reliability-related metrics of systems in different phases of their life cycle. For systems in the design phase, a Multi-Objective Genetic Algorithm (MOGA) is coupled with Discrete Event Simulation (DES) to provide non-dominated configurations with respect to availability and cost. The proposed MOGA + DES incorporates a Generalized Renewal Process to account for imperfect repairs and it also indicates the optimal number of maintenance teams. For the operational phase, a hybridism between MOGA and Risk-Based Inspection is proposed for the elaboration of non-dominated inspection plans in terms of risk and cost that comply with local regulations. Regression via Support Vector Machines (SVR) is applied when the reliability-related metric (response variable) of an operational system is function of a number of environmental and operational variables with unknown analytical relationship. A Particle Swarm Optimization is combined to SVR for the selection of the most relevant variables along with the tuning of the SVR hyperparameters that appear in its training problem. In order to assess the uncertainty related to the response variable, bootstrap methods are coupled with SVR to construct confidence and prediction intervals. Numerical experiments and application examples in the context of oil industry are provided. The obtained results indicate that the proposed frameworks give valuable information for budget planning and for the implementation of proper actions to avoid undesired events.