Dissertations / Theses on the topic 'Multi-objective methodology'

DI Diesel engine are widely used both for industrial and automotive applications due to their durability and fuel economy. Nonetheless, increasing environmental concerns force that type of engine to comply with increasingly demanding emission limits, so that, it has become mandatory to develop a robust design methodology of the DI Diesel combustion system focused on reduction of soot and NOx simultaneously while maintaining a reasonable fuel economy. In recent years, genetic algorithms and CFD three-dimensional combustion simulations have been successfully applied to that kind of problem. However, combining GAs optimization with actual CFD three-dimensional combustion simulations can be too onerous since a large number of calculations is usually needed for the genetic algorithm to converge, resulting in a high computational cost and, thus, limiting the suitability of this method for industrial processes. In order to make the optimization process less time-consuming, CFD simulations can be more conveniently used to generate a training set for the learning process of an artificial neural network which, once correctly trained, can be used to forecast the engine outputs as a function of the design parameters during a GA optimization performing a so-called virtual optimization. In the current work, a numerical methodology for the multi-objective virtual optimization of the combustion of an automotive DI Diesel engine, which relies on artificial neural networks and genetic algorithms, was developed.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2004.<br>Includes bibliographical references (p. 283-296).<br>(cont.) to the design of a heuristic decomposed algorithm for solving the ROP problem. This decomposition methodology offers an original paradigm potentially applicable to the design of solution algorithms for a class of problems with functions and parameters that, similar to those of the ROP problem, can be parsed in subsets. The potential merit in decomposing the ROP problem in two stages and the resulting utility of the two-stage solution algorithm are evaluated by performing benefits analysis across specific dimensions related to airport efficiency, as well as stability and robustness analysis of the algorithm output.<br>Significant delays and resulting environmental impacts are commonly observed during departure operations at major US and European airports. One approach for mitigating airport congestion and delays is to exercise tactical operations planning and control with an objective to improve the efficiency of surface and terminal area operations. As a subtask of planning airport surface operations, this thesis presents a thorough study of the structure and properties of the Runway Operations Planning (ROP) problem. Runway Operations Planning is a workload-intensive task for controllers because airport operations involve many parameters, such as departure demand level and timing that are typically characterized by a highly dynamic behavior. This research work provides insight to the nature of this task, by analyzing the different parameters involved in it and illuminating how they interact with each other and how they affect the main functions in the problem of planning operations at the runway, such as departure runway throughput and runway queuing delays. Analysis of the Runway Operations Planning problem revealed that there is a parameter of the problem, namely the demand "weight class mix", which: a) is more "dominant" on the problem performance functions that other parameters, b) changes value much slower than other parameters and c) its value is available earlier and with more certainty than the value of other parameters. These observations enabled the parsing of the set of functions and the set of parameters in subsets, so that the problem can be addressed sequentially in more than one stage where different parameter subsets are treated in different stages. Thus, a decomposition-based algorithm design technique was introduced and applied<br>by Ioannis D. Anagnostakis.<br>Ph.D.

Vibrations of a wind turbine have a negative impact on its performance and therefore approaches to effectively control turbine vibrations are sought by wind industry. The body of previous research on wind turbine vibrations has focused on physics-based models. Such models come with limitations as some ideal assumptions do not reflect reality. In this Thesis a data-driven approach to analyze the wind turbine vibrations is introduced. Improvements in the data collection of information system allow collection of large volumes of industrial process data. Although the sufficient information is contained in collected data, they cannot be fully utilized to solve the challenging industrial modeling issues. Data-mining is a novel science offers platform to identify models or recognize patterns from large data set. Various successful applications of data mining proved its capability in extracting models accurately describing the processes of interest. The vibrations of a wind turbine originate at various sources. This Thesis focuses on mitigating vibrations with wind turbine control. Data mining algorithms are utilized to construct vibration models of a wind turbine that are represented by two parameters, drive train acceleration and tower acceleration. An evolutionary strategy algorithm is employed to optimize the wind turbine performance expressed with three objectives, power generation, vibration of wind turbine drive train, and vibration of wind turbine tower. The methodology presented in this Thesis is applicable to industrial processes other than wind industry.

L'adoption d'une méthodologie est cruciale pour la conception de procédés chimiques optimaux. L'optimisation multi-objective de modèles rigoureux en est un exemple, jouissant d'une application extensive dans la littérature. Cette méthode retourne un ensemble de solutions, dit de Pareto, présentant un compromis optimal entre les fonctions objectives. Ceci est suivi par une étape de sélection d'une solution d'intérêt répondant à des critères définis. Cette méthodologie s'appliquait, dans le cadre de cette thèse, à deux procédés. Le premier consistait en une distillerie d'éthanol à partir de la canne à sucre, combinée à un système de cogénération et de combustion à partir de la biomasse de canne à sucre. Le deuxième contenait en plus un système d'hydrolyse enzymatique de cette biomasse. Notre première contribution traitait de la construction d'une procédure pour la modélisation, simulation, intégration thermique et évaluation du coût des équipements. La deuxième contribution traitait de l'analyse des résultats réalisée à travers un suivi de variables mesurées, une fragmentation de la courbe de Pareto, une hiérarchisation des variables de décision et une comparaison avec la littérature. La dernière contribution traitait de l'étape de sélection qui s'est réalisée à travers une évaluation économique des solutions, sous des scénarii différents, avec la Valeur Nette Présente comme critère de sélection. En conclusion, cette thèse constitue une première application intégrale de la méthodologie proposée. Elle représente, de par ses contributions, un tremplin pour des applications futures à des procédés chimiques ou biochimiques, plus spécialement pour la canne à sucre<br>The use of a systematic methodology is crucial for the design of optimal chemical processes, namely bio-processes. Multi-objective optimization of rigorous process models is therein a prime example, with extensive use in literature. This method yields a Pareto set of optimal compromise solutions, from which one optimal solution is chosen based on specific criteria. This methodology was applied, in the course of this thesis, to two studied processes. The first consisted in a distillery converting sugarcane to ethanol, combined with a sugarcane biomass combustion and power cogeneration system. The second contained an additional biomass hydrolysis system. Our first contribution deals with the construction of an organized procedure for the modeling, simulation, heat integration and equipment and capital cost estimation of chemical processes. The second contribution deals with the analysis of the optimization results through a tracking of measured variables, the fragmentation of the Pareto curve, an ordering of optimization variables, and a comparisons with literature results. The final realization deals with the selection step realized through an economic evaluation of optimal solutions for various scenarios, with the Net Present Value as the selection criterion. In conclusion, this thesis constitutes a first integral application of the said methodology. It sets, through its contributions, a stepping stone for future application in the field of chemical and biochemical processes, namely for sugarcane processes

Thesis (Ph.D)--Aerospace Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Mavris, Dimitri N.; Committee Member: German, Brian J.; Committee Member: Osburg, Jan; Committee Member: Ruffin, Stephen M.; Committee Member: Schrage, Daniel P.. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Orientador: Katia Lucchesi Cavalca<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica<br>Made available in DSpace on 2018-08-08T21:19:55Z (GMT). No. of bitstreams: 1 Escobar_RobertoLuiz_M.pdf: 2961640 bytes, checksum: 516985920427d6083c04c1c5a22d6470 (MD5) Previous issue date: 2007<br>Resumo: Os sistemas mecânicos são projetados para desempenhar funções específicas, e por essa razão as suas funções devem ser medidas para garantir seu desempenho dentro de uma certa precisão ou tolerância. A grande complexidade em se projetar e analisar novos projetos é a inserção de novas tecnologias, que envolvem aspectos multidisciplinares. Assim, o desenvolvimento e melhoria de projetos e produtos colocam o engenheiro projetista frente às diversas fontes de variabilidade, como por exemplo, as propriedades dos materiais, condições operacionais e ambientais e incertezas nas suposições feitas sobre seu funcionamento. Em termos de modelagem matemática, as aproximações inerentes e hipóteses feitas durante a concepção do sistema, conduzem normalmente a diferentes respostas obtidas através de simulações e/ou medidas experimentais. Dessa forma, em uma fase anterior à modelagem matemática,durante a concepção do sistema ou produto, as aplicações de ferramentas estatísticas e métodos de otimização podem fornecer estimativas sobre faixas de valores ou valores ótimos para parâmetros significativos de projeto, dentro do espaço experimental estudado. Esse tipo de abordagem estatística teve sua fundamentação teórica durante as décadas de 20 e 30 por Fisher, com a aplicação da teoria estatística sob diversos aspectos, como por exemplo: testes de hipóteses, estimativa de parâmetros, seleção de modelos, planejamento experimental e, mais tarde, no controle e melhoria de processos e produtos. Assim, este trabalho propõe um procedimento de estudo e otimização, integrando a teoria de planejamento experimental, a metodologia da superfície de resposta e otimização multi-objetivos através de algoritmos genéticos, para se obter a otimização dos parâmetros de projeto de componentes mecânicos. Em específico, foram utilizados dados de um sistema rotor-mancal e o estudo implica em minimizar as amplitudes no domínio da freqüência. Outro objetivo deste trabalho, foi desenvolver um programa para otimização multi-objetivos através de algoritmos genéticos<br>Abstract: The mechanical systems are designed to be applied to any specific situations, and in this waytheir features should be measured to guarantee confidence to the systems. Their development and analysis expose the designer to a series of unknown parameters from several sources such as material properties, environmental and operational conditions. In terms of mathematical modeling, the inherent approximation and hypotheses made during system conception lead to different responses obtained by simulations and/or experimental measurements. So, in a previous phase of mathematical modeling, during the design analysis, the application of statistical tools and optimization methods is possible to estimate the values and/or ranges of the critical design parameters inside an experimental space. The connection between optimization and statistical data back at least to the early part of the 20th century and encompasses many aspects of applied and theoretical statistics, including hypothesis testing, parameter estimation, model selection, design of experiments and process and product control. So, this work proposes a link between theory of design of experiments, response surface methodology and multi-objective optimization using genetic algorithms, in order to optimize parameters for mechanical components. This study makes possible to verify the application of multi-objective optimization using genetic algorithms in design parameters and optimize them. A rotor-bearing system was used and amplitude in frequency domain was minimized. An experimental software for multi-objective optimization using genetic algorithm was developed.<br>Mestrado<br>Mecanica dos Sólidos e Projeto Mecanico<br>Mestre em Engenharia Mecânica

The aim of this work is determine the most appropriated period for connection of a particular generation source fueled by biogas on a distribution network. The biogas is produced through anaerobic reactions that occur in the municipal solid waste deposited at the landfill specific landfill in analysis. The Minas do Leão landfill is taken as study case, predicting the connection of this generation source on a distribution network supported by the State Company of Electrical Energy (CEEE). The Biogas® software was used to estimate the amount of methane generated in the landfill and to determine the landfill life cycle. By using that information it is possible to plot the power electrical generation curve in MW. The choice for the Biogas® software is justified because this tool is free software developed between the federal government environment state administration and the CETESB (Technology Company of Environmental Sanitation of the State of Sao Paulo). After that, the PSL®DMS software was used to simulate the main electrical characteristics of the network in analysis. These simulations provided the data for evaluating the quantitative parameters voltage levels, power losses and load current. A group of decision makers were selected for establishing scores applied to the qualitative parameter evaluation - availability of ancillary services support. After the definition of the quantitative and qualitative parameters a multiobjective methodology was developed. In the first step of the methodology are applied pertinent fuzzy functions trapezoidal variables that consider electrical characteristics of the network and also the voltage levels defined in the PRODIST (Proceedings of Electric Energy Distribution on Electrical National System). The second part is associated with the decision makers estimative, in which the decision maker presents some scores for each type of ancillary service in analysis, according to the specific moment when this ancillary service will be supported. For these evaluations are considered the demand curve of the feeder where the distributed source will be connected. The methodology was then applied for selecting and ranking the most appropriated period to connect the distributed generation source on the distribution network. The definition of the ranking is the outcome of the data evaluation according to the final priorities quantitative and qualitative analysis.<br>Este trabalho tem a finalidade de definir o período do dia para o despacho de uma fonte alternativa de geração distribuída de energia elétrica (GD), utilizando como combustível o biogás proveniente de reações anaeróbias que ocorrem em resíduos sólidos urbanos (RSU) depositados em células do Aterro Sanitário de Recreio no município de Minas do Leão à rede da Companhia Estadual de Distribuição de Energia Elétrica (CEEE-D). Inicialmente foi utilizado o software Biogás® para estimar a vazão de metano (gás em maior porcentagem no biogás) e avaliar a potência que este gás disponibiliza para ser gerada em qualquer aterro em função do tempo de vida útil do mesmo. Este software foi escolhido para ser utilizado neste trabalho por ser uma ferramenta livre e desenvolvida no Brasil pelo convênio entre a Secretaria de Estado do Meio Ambiente e da Companhia de Tecnologia de Saneamento Ambiental do Estado de São Paulo (CETESB) com o Governo Federal, por meio do Ministério da Ciência e Tecnologia. Esta recuperação energética do metano tem potencialidade de servir como combustível para alimentar um ou mais conjuntos de motores geradores Ciclo Otto adaptados para biogás. Após a estimativa da vazão de biogás e da potência que pode ser gerada no aterro, partiu-se para a execução de simulações com as grandezas elétricas do alimentador. Neste trabalho as simulações das grandezas elétricas foram obtidas com o uso do software PSL®DMS, ferramenta utilizado pelos técnicos da CEEE-D para análise da do fluxo de carga de sistemas de distribuição, e serão utilizadas como critérios denominados de quantitativos. Para determinar o momento ideal para a conexão da GD à rede de distribuição da concessionária, foi utilizado um método de apoio à decisão. Este método considera critérios quantitativos e qualitativos definidos pelos Especialistas ou Agentes de Decisão (AD), no caso, técnicos da concessionária de energia elétrica que avaliam em conjunto estes dados. Os critérios qualitativos são baseados na possibilidade de incrementos de serviços subsidiários ao sistema de distribuição de energia elétrica, denominados de Serviços Ancilares (SA). A definição da prioridade dos valores dos critérios quantitativos (grandezas elétricas da rede de distribuição) é baseada na análise de funções trapezoidais que relacionam as grandezas elétricas a valores e percentuais definidos pelo PRODIST e pelos AD. Já para os critérios qualitativos, a análise ocorre em função de uma metodologia denominada de Estimativa de Especialistas, em que os mesmos aplicam notas ao tipo de SA que será aplicado e ao momento de sua aplicação em relação às características da curva de demanda do AL. A definição do ranking dos melhores horários do dia para despacho da GD ao AL é consequência da avaliação dos valores estabelecidos para as prioridades finais dos critérios quantitativos e qualitativos.

Les procédés de fabrication de pièces mécaniques par enlèvement de matière (tournage, fraisage, perçage, ...) connaissent une utilisation massive dans l’industrie aéronautique et l’automobile. Les pièces obtenues par ces procédés doivent satisfaire à des propriétés géométriques, métallurgiques et à des caractéristiques de qualité. Pour répondre à ces exigences, plusieurs essais expérimentaux basés sur le choix des conditions de coupe sont souvent nécessaires avant d’aboutir à une pièce satisfaisante. Actuellement, ces méthodes empiriques basées sur l’expérience des fabricants et des utilisateurs des outils coupants sont souvent très longues et coûteuses, donnent une large plage de choix des paramètres en fonction de leurs besoins. Toutefois, le coût très élevé d’un essai limite fondamentalement le nombre d’expériences, avoir une pièce respectant les caractéristiques souhaitées avec un coût acceptable devient une tâche difficile<br>Manufacturing processes of mechanical parts by removal of material (turning, milling, drilling ...) have extensive use in aeronautic and automobile industry. The components obtained using these methods must satisfy geometric properties, metallurgical and quality characteristics. To meet these requirements, several experimental tests based on the selection of cutting conditions are often necessary before manufacturing. Currently, these empirical methods based on the experience of manufacturers and users of cutting tools (charts, diagrams with experimental findings, ...) are often very lengthy and costly. However, the high cost of a trial limits the number of experiments, so to have a deserted component with an acceptable cost is a difficult task. The importance of cutting conditions monitored by limitations is related to the type of material to be machined, since it determines the behavior of the machining

AlNiCo magnets are known for high-temperature stability and superior corrosion resistance and have been widely used for various applications. Reported magnetic energy density ((BH) max) for these magnets is around 10 MGOe. Theoretical calculations show that ((BH) max) of 20 MGOe is achievable which will be helpful in covering the gap between AlNiCo and Rare-Earth Elements (REE) based magnets. An extended family of AlNiCo alloys was studied in this dissertation that consists of eight elements, and hence it is important to determine composition-property relationship between each of the alloying elements and their influence on the bulk properties. In the present research, we proposed a novel approach to efficiently use a set of computational tools based on several concepts of artificial intelligence to address a complex problem of design and optimization of high temperature REE-free magnetic alloys. A multi-dimensional random number generation algorithm was used to generate the initial set of chemical concentrations. These alloys were then examined for phase equilibria and associated magnetic properties as a screening tool to form the initial set of alloy. These alloys were manufactured and tested for desired properties. These properties were fitted with a set of multi-dimensional response surfaces and the most accurate meta-models were chosen for prediction. These properties were simultaneously extremized by utilizing a set of multi-objective optimization algorithm. This provided a set of concentrations of each of the alloying elements for optimized properties. A few of the best predicted Pareto-optimal alloy compositions were then manufactured and tested to evaluate the predicted properties. These alloys were then added to the existing data set and used to improve the accuracy of meta-models. The multi-objective optimizer then used the new meta-models to find a new set of improved Pareto-optimized chemical concentrations. This design cycle was repeated twelve times in this work. Several of these Pareto-optimized alloys outperformed most of the candidate alloys on most of the objectives. Unsupervised learning methods such as Principal Component Analysis (PCA) and Heirarchical Cluster Analysis (HCA) were used to discover various patterns within the dataset. This proves the efficacy of the combined meta-modeling and experimental approach in design optimization of magnetic alloys.

This study is concerned with the problem of resource-constrained project scheduling which includes splittable and nonsplittable jobs, renewable and nonrenewable resources, variation in resource availabi1ity, time-resource tradeoff, time-cost tradeoff, and multiple objectives. The problem is formulated as a zero-one integer programming model. A specialized solution technique is developed for the preemptive goal programming, resource-constrained project. scheduling problem for time, cost, and resource leveling objectives. In addition, single objective algorithms are also provided for the time, cost, and resource leveling objectives. These algorithms are based on the idea of the implicit enumeration process, and use the special structures of the problem to expedite the search process. Computer-generated problems are used to test each of the single objective algorithms. The results show that the algorithms give optimal solutions to tested problems with time and cost objectives using a reasonable computation time; however, heuristic solutions are more feasible for problems with resource leveling objective. The multiple objective algorithm is illustrated through application to a warehouse project problem.<br>Graduation date: 1993

碩士<br>國立勤益科技大學<br>電機工程系<br>103<br>This paper aims to multi-objective optimized design of three-phase Permanent Magnet Linear Synchronous Motor (PMLSM). The purpose of this dissertation is to provide use of Response Surface Methodology (RSM) in decreasing its cogging force, and retain its back-electromotive force (back-EMF) and force so as to achieve the multi-objective optimized result. PMLSM have two factors that affect the motor performance. Harmonic components that caused commutation ripple-force by the impressed voltage and current, between the slot and the stator magnet are generating cogging-force, respectively. These two factors are the main cause of the ripple-force. The driver is limited by commutation rippler-force of the performance. The cogging force can use new magnet types to improve. This paper uses the Maxwell 2D Finite Element Method electromagnetic field simulation analysis software developed by Ansoft (U.S.). Through the comparison between simulation results and prototypes, and evaluate the performance of PMLSM. Focuses on the optimization design of permanent magnet, in the design of permanent magnet, the variable thickness of permanent magnet (Δhm), maximum thickness of permanent magnet (Δm), harmonic amplitude and magnet width were considered for optimal design of magnet. These parameters affect on the motor can not be learn directly from the formula, so must match the response surface method of regression model optimization mode, find the most suitable combination. In the end, analyze and comparison. And then, use anylsis methods to compare, and prove effective is better.

碩士<br>國立勤益科技大學<br>電機工程系<br>102<br>This study used the two-dimensional finite element analysis (2-D FEA) software Maxwell 2D to analyze the electromagnetic field and performance characteristics of an elevator-door-roller motor. The primary purpose of this study was to reduce the cogging torque. These cogging torque reducing methods are discussed in this study. These methods included the fractional-slot winding, optimal pole arc coefficient, permanent magnet arc suppression, stator chute, skew rotor pole, magnet pole shifting, magnet pole segment dislocation, dummy slot and slot opening width. The secondary purpose was to improve the efficiency and reduce the torque ripple of the motor. The multi-objective optimization analysis was conducted after the three design objectives have met the needs of the manufacturers. First, this study determined the design objectives and the number of design factors. The design factors that have a higher degree of impact on the design objectives were obtained through the regression analysis by R language. The suitable orthogonal array was selected. Then the correlation degree was defined between -1 to +1 by the extension theory. The multi-objective optimization design parameters were derived by applying the concepts of the extension theory and the mean value analysis of Taguchi method. Finally, the 2-D FEA was applied to analyze the prototype and optimal structures, and verify whether they meet the design specifications.

碩士<br>國立高雄大學<br>亞太工商管理學系碩士班<br>97<br>Taguchi Method’s Robust Design has been applied in a various range of industrial applications. However, the major part only aims at the question of single performance characteristics optimization. More than one quality characteristics must be simultaneously considered to effectively improve the product quality due to the increasing complexity of product design. Optimizing each quality characteristic separately may not result the optimality for the entire production. More often, considering one quality characteristic may conflict with the other. The more the number of the performance characteristics, the stronger the correlation of them. It also brings the engineer the difficulties of resolving the performance characteristics conflict. This article undertakes the Optimization of multiple quality characteristics using the combination of Taguchi Method and Grey Relational Analysis. If the factorial level belongs to the continuous factor, this work will find the best setup according to the Response Surface Methodology to confirm the valuable combination. In order to show the effectiveness and practicality of the proposed algorithm, this article uses the two cases – Substrate Laser Displacement Measurement and Optimal Coating of Photoresists of LCD process respectively. The results show that this method can find the best production process parameter level combination of the two processes. Demonstrating the effectiveness of the research, helps the company to improve the quality of product effectively.

Doctoral Thesis in Civil Engineering<br>The expansion and quality increase of road infrastructures in developed countries during the last decades is shifting the attention towards their preservation rather than to new construction. Pavements are the most costly road asset. Therefore, their preservation management optimization is important in order to meet quality and safety demands within available budgets that are becoming increasingly limited. More recently, environmental aspects related to the pavements life-cycle have been raising concerns that must be addressed. The present thesis describes the development of an optimization methodology that intends to be a decision support tool for road administrations. In fact, this work emerged within the scope of a highway administration related project, so it has a character of applied research. The developed methodology is capable of suggesting pavement preservation plans at the network level for a certain time period. It considers aspects related to pavement quality, administration costs, as well as user and environmental costs. Genetic algorithms are employed to optimize problems with two or three con icting objectives, either simultaneously or at distinct phases in order to deal with pairs of objectives at di erent levels. Data selection and ltering and the development of Markov based prediction models are also included in the thesis. The methodology is exible, so that other prediction models with better adjustment to the problem to be solved can be implemented. Similarly, preservation operations and the respective costs and e ects on the pavement are also adjustable. The use of European-level standardized performance indicators and the consideration of user costs and costs related to CO2 emissions in a multi-objective optimization are the main di erences of this methodology to existent ones. The methodology is applied in two distinct case studies that complement each other, allowing to demonstrate the features and adaptability of the methodology. The rst case study relies on historical data from an American database, while the second one is based on data provided by a Portuguese highway administration. In the rst case, preservation plans for a group of geographically separated pavement sections are de ned, regarding administration costs and pavement quality at rst, and user and environmental costs at last. The second case study is focused on a pavement segment that is divided in several dozens of adjacent sections. This allows the suggestion of preservation plans indicating the relative segment area that should be a ected with a speci c treatment in a speci c year, in order to meet quality constraints while optimizing administration costs and pavement quality for the analysis period.<br>O grande desenvolvimento verificado na expansão e na qualidade das infraestruturas rodoviárias nas últimas décadas em países desenvolvidos, faz com que cada vez seja dada mais atenção à sua preservação do que à nova construção. Os pavimentos rodoviários são o ativo mais dispendioso das estradas, pelo que é de extrema importância otimizar a gestão da sua conservação de modo a cumprir com critérios de qualidade e segurança dentro dos limites orçamentais que tendem a ser cada vez mais restritos. Mais recentemente, questões ambientais associadas ao ciclo de vida dos pavimentos rodoviários têm levantado preocupações que devem ser abordadas. Esta tese descreve o desenvolvimento de uma metodologia de otimização que pretende constituir uma ferramenta de apoio à decisão para administrações rodoviárias. De facto, este trabalho surgiu no âmbito de um projeto associado a uma concessionária de auto-estradas, pelo que tem um caracter de investigação aplicada. A metodologia desenvolvida é capaz de sugerir planos de conservação de pavimentos ao nível da rede para um determinado período de tempo, tendo em conta aspetos relacionados com a qualidade do pavimento, custos para as concessionárias, bem como custos para os utentes da estrada e para o ambiente. A metodologia usa algoritmos genéticos para otimizar problemas com dois ou três objetivos conflituantes, quer em simultâneo, quer em fases distintas de modo a lidar com pares de objetivos em diferentes níveis. A tese inclui ainda uma etapa de seleção e tratamento de dados, bem como de desenvolvimento de modelos de previsão baseados em cadeias de Markov. A metodologia é exigível e pode ser alimentada com outros modelos de previsão que melhor se ajustem ao problema em causa. De igual modo, também as operações de conservação, respetivos custos e efeitos no pavimento são ajustáveis. O uso de indicadores de desempenho uniformizados ao nível Europeu, e a inclusão de custos para os utilizadores das estradas e de custos associados a emissões de CO2 numa otimização multi-objectivo são as principais diferenças desta metodologia para outras já existentes na literatura. A metodologia é aplicada a dois casos de estudo distintos que se complementam e permitem demonstrar as funcionalidades e flexibilidade da metodologia. O primeiro caso de estudo baseia-se em dados históricos de uma base de dados Americana, e o segundo em dados fornecidos por uma concessionária de autoestradas Portuguesa. No primeiro caso, são definidos planos de conservação para um conjunto de secções de pavimento separadas geograficamente, tendo em conta custos para a administração e qualidade do pavimento, primeiramente, e custos ambientais e para o utilizador, por último. O segundo caso de estudo foca-se numa extensão de pavimento dividida em várias dezenas de secções adjacentes, permitindo assim definir planos de conservação que indicam percentagens dessa extensão que deverão receber um determinado tratamento em determinado ano, de modo a otimizar a qualidade e os custos para a concessionária, cumprindo com as restrições de qualidade para todo o período de análise.<br>This research work was nanced by the Ministry of Education and Science of the Government of Portugal and by the European Social Fund of the European Union (ESF/EU) through the Foundation for Science and Technology (FCT) with the Ph.D. grant SFRH/BD/85984/2012.