In network design, a communication network is optimized for a given set of parameters like cost, reliability and delay. This study analyzes network design problem using Genetic Algorithms in detail and makes comparison of different approaches and representations. Encoding of a problem is one of the most crucial design choices in Genetic Algorithms. For network design problem, this study compares adjacency matrix representation with list of edges representation. Also, another problem is defining a fair fitness function that will not favor one optimization parameter to the other. Multi-objective optimization is a recommended solution for such problems. This study describes and compares some of those approaches for different combinations in network design problem.

Reservoir management typically focuses on maximizing oil and gas recovery from a reservoir based on facts and information while minimizing capital and operating investments. Modern reservoir management uses history-matched simulation model to predict the range of recovery or to provide the economic assessment of different field development strategies. Geological models are becoming increasingly complex and more detailed with several hundred thousand to million cells, which include large sets of subsurface uncertainties. Current issues associated with history matching, therefore, involve extensive computation (flow simulations) time, preserving geologic realism, and non-uniqueness problem. Many of recent rate optimization methods utilize constrained optimization techniques, often making them inaccessible for field reservoir management. Field-scale rate optimization problems involve highly complex reservoir models, production and facilities constraints and a large number of unknowns. We present a hierarchical multiscale calibration approach using global and local updates in coarse and fine grid. We incorporate a multiscale framework into hierarchical updates: global and local updates. In global update we calibrate large-scale parameters to match global field-level energy (pressure), which is followed by local update where we match well-by-well performances by calibration of local cell properties. The inclusion of multiscale calibration, integrating production data in coarse grid and successively finer grids sequentially, is critical for history matching high-resolution geologic models through significant reduction in simulation time. For rate optimization, we develop a hierarchical analytical method using streamline-assisted flood efficiency maps. The proposed approach avoids use of complex optimization tools; rather we emphasize the visual and the intuitive appeal of streamline method and utilize analytic solutions derived from relationship between streamline time of flight and flow rates. The proposed approach is analytic, easy to implement and well-suited for large-scale field applications. Finally, we present a hierarchical Pareto-based approach to history matching under conflicting information. In this work we focus on multiobjective optimization problem, particularly conflicting multiple objectives during history matching of reservoir performances. We incorporate Pareto-based multiobjective evolutionary algorithm and Grid Connectivity-based Transformation (GCT) to account for history matching with conflicting information. The power and effectiveness of our approaches have been demonstrated using both synthetic and real field cases.

Orientador: José Roberto Sanches Mantovani / Resumo: Neste trabalho é proposto o desenvolvimento de uma ferramenta computacional para o planeja-mento e despacho ótimo de fontes de potência ativa, considerando as incertezas das cargas (le-ve, nominal e pesada) e fontes de energia renováveis não despacháveis através de uma aborda-gem probabilística. O modelo matemático é um problema de programação não linear inteiro misto, multiobjetivo, não convexo e probabilístico na sua forma original sem a necessidade de realizar qualquer tipo de simplificação ou linearização tanto das funções objetivo como das res-trições. Um algoritmo baseado na meta-heurística Non-dominated Sorting Genetic Algorithm (NSGA-II) é proposto para resolver o problema de maneira eficaz. Os resultados obtidos com as simulações realizadas usando a implementação computacional nos sistemas de testes IEEE30 barras e IEEE118 barras mostram a eficiência e robustez da metodologia proposta. / Abstract: This work proposes the development of a computational tool for the planning and optimal dispatch of active power sources, considering the uncertainties of the loads (light, nominal and heavy) and non-dispatchable renewable energy sources through a probabilistic approach. The mathematical model is a multi-objective mixed-integer nonlinear programing problem, that is nonconvex and probabilistic in its original form, without the need to perform any kind of simplification or linearization of both objective functions and constraints. An algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) meta-heuristic is pro-posed to solve the problem effectively. The results obtained with the simulations performed using the computational implementation in the IEEE30 bus and IEEE118 bus test systems show the efficiency and robustness of the proposed methodology. / Doutor

Diante dos desafios impostos pelo sistema econômico, características dos mercados e exigências dos clientes, as empresas são forçadas a operar com lotes de produção cada vez menores, dificultando a gestão de operações e a otimização dos sistemas produtivos. Desse modo, intensifica-se nos meios corporativos e acadêmicos a busca por abordagens que possibilitem a criação de diferenciais competitivos de mercado, sendo esta a justificativa prática deste trabalho, que propõe uma sistemática integrada para alocação, sequenciamento e balanceamento de lotes em um horizonte de programação em múltiplas linhas de produção em um sistema multiproduto com operadores polivalentes. A sistemática proposta foi dividida em três fases. A primeira fase utiliza um algoritmo genético multiobjetivo com o intuito de determinar a linha de produção em que cada lote será produzido. A segunda fase é responsável pelo sequenciamento dos lotes produtivos e se apoia em uma alteração da regra Apparent Tardiness Cost (ATC). Na terceira fase utilizou-se o método Ranked Positional Weight (RPW) para balancear a distribuição das tarefas entre os operadores polivalentes de cada linha de produção, respeitando a precedência das tarefas. A sistemática foi aplicada em dados reais do segmento têxtil, aprimorando os indicadores produtivos e de entrega e conferindo maior flexibilidade ao processo frente à demanda sazonal. / Faced with the challenges imposed by the economic system, characteristic of the markets and requirements of the customers, the companies are forced to operate with smaller production batches, making it difficult to manage operations and optimization of the production systems. In this way, the search for improvements that allow the creation of competitive differentials of market is intensified in the corporate and academic circles. This is the practical justification for this work, which proposes an integrated systematics for the allocation, sequencing and balancing of batches in a horizon of programming in multiple production lines in a multiproduct system with multipurpose operators. The systematic proposal was divided into three phases. The first phase uses a multiobjective genetic algorithm with intention to determine the production line in which each batch will be produced. The second phase is responsible for the sequencing of productive batches and is based on a change in the rule Apparent Tardiness Cost (ATC). In the third phase the method Ranked Positional Weight (RPW) was used to balance the distribution of the tasks between the multipurpose operators of each line of production, respecting the precedence of the tasks. The systematics was applied in real data of the textile segment, improving the productive and delivery indicators and giving greater flexibility of the process against the seasonal demand.

Notre travail de recherche traite la problématique de la conception d’une chaîne logistique multi-niveaux tout en tenant compte du cycle de vie du produit. Par cycle de vie du produit, nous voulons dire la succession des quatre phases de commercialisation que traverse un produit à travers le temps, à savoir : l’introduction, la croissance, la maturité et le déclin. L’objectif est de mette en place un modèle mathématique qui soit fondé sur une analyse approfondie des différents acteurs de la chaîne, selon la phase du cycle de vie du produit.Trois principaux modèles ont été développés dans cette thèse. Chacun fait l’objet d’un chapitre à part entière.Le premier modèle développé vise à concevoir une chaîne logistique de coût minimum, tout en prenant en considération l’efficacité des différents acteurs potentiels calculée selon plusieurs critères (coût, qualité, innovation, qualité du service, délais de livraisons, …), ainsi que sa variation au cours du cycle de vie du produit. Un deuxième modèle a été mis en place pour la conception d’une chaîne logistique durable, tout en prenant en considération le cycle de vie du produit. Dans ce modèle, trois objectifs différents ont été pris en compte à la fois, à savoir, un objectif économique, un objectif environnemental et un objectif social. Dans les deux premiers modèles, nous avons supposé que le produit aura un cycle de vie classique. Cependant, dans la réalité, ceci n’est pas toujours le cas. En effet, quelques produits connaissent des cycles de vie très atypiques et donc très éloignés de la courbe d’un cycle de vie théorique. Pour ce faire, un troisième modèle stochastique a été proposé pour la conception d’une chaîne logistique robuste, tenant compte des différents scénarios du cycle de vie du produit. / Our research addresses the problem of designing a multi-level supply chain, while taking into consideration the product life cycle. By product life cycle, we mean the succession of the four marketing stages that a product goes through since its introduction to the market and until it will be removed from. All products have a life cycle which can be classified into four discrete stages: introduction, growth, maturity and decline.Depending on the product life cycle phases, and based on a thorough analysis of the different supply chain potential actors, this study aims to establish mathematical models to design an efficient supply chain network. Three main models have been developed in this thesis. The first proposed model aims to design a product-driven supply chain with a minimal total cost, taking into consideration the evaluation of the different potential actors effectiveness, according to several criteria (cost, quality, innovation, quality service, timely delivery, ...).A second model was developed to design of a sustainable supply chain network, taking into account the product life cycle. In this model, three different objectives at the time were considered, namely, an economic objective, an environmental objective and a social objective.In the two previous models, we have assumed that the product has a classical life cycle. However, in the reality this is not always the case. Indeed, some products have very atypical life cycles, whose curves are very different from the classical one. To tackle this problem, in the third part of this thesis, we propose a stochastic model to design a robust supply chain network, taking into account the different product life cycle scenarios.

Cette thèse suit une démarche « materials-by-design » avec pour objectif le développement d'une méthode de conception dédiée aux panneaux sandwichs architecturés pour l'obtention de propriétés multifonctionnelles. Cette méthode s'appuie sur l'utilisation d'un algorithme génétique permettant simultanément une sélection de matériaux (variables discrètes) et un pré-dimensionnement du panneau (variables continues). Trois architectures de cœur ont été étudiées : les mousses, les nids-d'abeilles hexagonaux et les treillis tétraédriques. Dans cette thèse, on définit deux approches différentes de sélection des matériaux. Dans un premier temps, les matériaux architecturés sont considérés comme des matériaux existants, dont les propriétés sont référencées dans une base de données fermée. Cette approche est appelée optimisation par « voie réelle ». Afin d'ouvrir les possibilités en termes de sélection de matériaux, la deuxième approche considère une description semi-continue des matériaux architecturés et est appelée optimisation par « voie virtuelle ». Le matériau cœur est décrit par un matériau constitutif (variable discrète) et par une ou plusieurs variables géométriques continues représentant l'architecture. Utilisant ces deux approches, différentes propriétés d'emploi des panneaux sandwichs sont évaluées : rigidité et résistance en flexion, atténuation acoustique, résistance et isolation thermique, et enfin résistance aux chocs impulsionnels. Chaque fonction est optimisée à masse minimale par optimisation bi-objectifs. Différents cas d'optimisation tri-objectifs sont également présentés afin d'évaluer la compatibilité entre propriétés. En effet, la forme de la surface de compromis obtenue donne une indication sur la compatibilité entre les différents critères. Cette étape d'optimisation permet également l'identification des paramètres de conception optimaux. Dans le cas d'une optimisation par « voie virtuelle », une comparaison directe entre architectures est aussi possible. Cependant, la démarche d'optimisation mise en place est complexe car globale et travaillant avec des variables mixtes. Deux méthodes mixtes, couplant l'algorithme génétique avec d'autres approches, sont proposées pour permettre un accroissement de la complexité de l'analyse tout en garantissant une complexité raisonnable de l'optimisation. / The present thesis aims at developing a design method dedicated to the optimization of architectured sandwich panels for multifunctional properties following a “materials-by-design” approach. This method is based on a genetic algorithm which enables to deal with materials selection (discrete variables) and geometrical dimensioning (continuous variables) simultaneously. Three core architectures have been investigated: foams, hexagonal honeycombs and tetrahedral truss structures. In this thesis, two main paths for material selection are defined. In the first one, architectured materials are considered as existing materials with properties referenced in a closed materials database. This is called the “real path” optimization. In order to expand the range of possibilities in terms of materials selection, a semi-continuous description of the architectured materials is considered in the second path, which is called “virtual path” optimization. The core material is described by a constitutive material (discrete variable) and a set of continuous geometrical variables representing the architecture. Using these two aforementioned approaches, several working properties of sandwich panels have been evaluated: flexural stiffness and strength, acoustic damping, thermal resistance and insulation, and finally blast mitigation. Bi-objective optimizations were performed in order to optimize each property in a minimal weight design. Some tri-objective cases are also presented, thus assessing the compatibility between different specifications. Indeed, this is achieved by relating trade-off surface shape to the compatibility between specifications. The optimization results also help identify the optimal design regarding the different criteria. Using the “virtual path” approach, a direct comparison between the different core architectures is achievable. Nevertheless, by being global and dealing with mixed variables, the obtained optimization process is complex. Two mixed methods where genetic algorithm is coupled with other approaches are proposed in order to increase the analysis complexity while providing a reasonable optimization complexity.

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A operação eficiente do sistema é uma ferramenta fundamental para que sua vida útil se prolongue o máximo possível, garantindo o perfeito atendimento aos consumidores, além de manter os custos com energia elétrica e manutenção dentro de padrões aceitáveis. Para uma eficiente operação, é fundamental o conhecimento do sistema, pois, através deste, com ferramentas como modelos de simulação hidráulica, otimização e definição de regras, é possível fornecer ao operador condições de operacionalidade das unidades do sistema de forma racional, não dependendo exclusivamente de sua experiência pessoal, mantendo a confiabilidade do mesmo. Neste trabalho é desenvolvido um modelo computacional direcionado ao controle operacional ótimo de sistemas de macro distribuição de água potável, utilizando um simulador hidráulico, um algoritmo de otimização, considerando dois objetivos (custos de energia elétrica e benefícios hidráulicos) e um algoritmo de aprendizado para extração de regras operacionais para o sistema. Os estudos foram aplicados no sistema de macro distribuição da cidade de Goiânia. Os resultados demonstraram que podem ser produzidas estratégias operacionais satisfatórias para o sistema em substituição ao julgamento pessoal do operador. / The efficient operation of a system is a fundamental tool to postpone the system’s service life as much as possible, thus ensuring a good service to the consumer while keeping electrical energy and maintenance costs at acceptable levels. Efficient operation requires knowledge of the system, for this knowledge, supported by tools such as models for hydraulic simulation, optimization, and definition of rules, provides the operator with proper conditions for the rational operating of the system’s units without depending exclusively on personal experience while maintaining the system’s reliability. In this work is developed a computational model for the optimal operation control of macro water distribution systems using a hydraulic simulator, an optimization algorithm, and a learn algorithm to extract operational rules (strategies) for the system. These studies are to be based on the macro system of the city of Goiânia, in Brazil. The results show that solutions for satisfactory operation can be quickly produced as a substitute to the personal judgment of the operator.

Orientadores: Christiano Lyra Filho, Celso Cavellucci / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-25T04:13:51Z (GMT). No. of bitstreams: 1 Assis_LauraSilvade_D.pdf: 3122445 bytes, checksum: 01644f90a086983b8729f81804874faa (MD5) Previous issue date: 2014 / Resumo: Grande parte das falhas em sistemas elétricos de potência ocorrem por consequência de falhas permanentes nas redes de distribuição. Agências reguladoras definem índices de confiabilidade para quantificar e avaliar a qualidade da distribuição de energia. A violação dos limites estabelecidos podem resultar em multas significativas para a distribuidora de energia. Um dos objetivos ao se realizar a instalação de chaves em redes de distribuição é criar a possibilidade de re-energizar a maior quantidade de clientes no menor tempo possível através da transferência de carga para sistemas que não tiveram seu fornecimento de energia interrompido. Esta tese estuda o problema de alocação de chaves (PAC) em sistemas radiais de distribuição de energia elétrica e propõe a instalação otimizada desses dispositivos em locais apropriados das redes, a fim de melhorar a confiabilidade do sistema pela redução do período que os consumidores ficam sem energia. Uma metodologia baseada nos conceitos de algoritmo memético juntamente com uma população estruturada é proposta neste trabalho para alocar chaves seccionadoras e de manobra, manuais e automáticas, com diferentes capacidades. A função objetivo utilizada busca minimizar o custo de alocação das chaves e o custo da energia não distribuída sob restrições de confiabilidade e fluxo de carga em todos os componentes da rede. É apresentado também um estudo multiobjetivo para o PAC, que procura alocar chaves minimizando simultaneamente os custos de instalação das chaves e da energia não distribuída e maximizando a confiabilidade da rede, sob restrições de fluxos. A abordagem proposta para resolver o PAC mono-objetivo também foi utilizada no PAC multiobjetivo, juntamente com o método do ?-restrito. A metodologia proposta tem o seu bom desempenho confirmado por diferentes estudos de casos com redes reais de grande porte localizadas no estado de São Paulo / Abstract: Most failures in electric power systems occur as a result of permanent faults in distribution networks. Regulatory agencies establish reliability standards indices for quantify and evaluate the quality of power distribution. The infringe of established limits can result in costly fines for the utility suppliers. One of the aim when perform the switches allocation in distribution networks is the possibility of re-energize the largest amount of customers in the shortest possible time by transferring load to other power systems which don¿t had their energy supply interrupted. This thesis studies the switch allocation problem (SAP) in radial systems of electrical power distribution and proposes an optimized installation of these devices in appropriate locations of network, in order to improve the reliability system by the reducing of the period that consumers remains without power. A methodology based on the concepts of memetic algorithm with a structured population is proposed in this thesis to allocate sectionalizing and tie switches of different capacities, with manual or automatic operation schemes. The objective function used seeks to minimize the switches allocation and the energy not supplied costs under constraints of reliability and load flow. A Multi-objective study for SAP is presented, to perform the switches allocation seeks minimize simultaneously the switches installation and energy not supplied costs and maximize the network reliability, under constraints of load flow. The proposed approach to solve the SAP monocriteria was also used in SAP multi-criteria along with the ?-constraint method. The proposed methodology has its good performance confirmed by several case studies with real large networks located in the state of São Paulo / Doutorado / Automação / Doutora em Engenharia Elétrica

Depuis le début des années 2000, le secteur social et médico-social connait des évolutions et des mutations importantes. D’un côté, le nombre de personnes prises en charge est en perpétuelle augmentation. D’un autre côté, les finances et les budgets mis à disposition des établissements ne cessent de se réduire, ce qui oblige les décideurs à s’adapter et à trouver de nouvelles solutions pour faire plus avec moins de moyens. Dans cette thèse, nous étudions un problème pratique auquel sont souvent confrontés les directeurs des établissements qui est l’élaboration de plans d’actions optimaux. Un plan d’actions est un ensemble d’actions qui sont mises en place afin d’améliorer à la fois les performances de l’établissement et la qualité de prise en charge de ses résidents.Élaborer un plan d’actions optimal consiste à identifier et choisir les meilleures actions qui améliorent tous les objectifs du plan tout en respectant quelques contraintes. Après la présentation du contexte pratique et théorique, nous fournissons une modélisation formelle du problème sous forme d’un problème de sac-à-dos multi-objectif.Puis nous présentons quelques méthodes de résolution à base d’indicateurs de qualité et de la dominance de Lorenz. Nous montrons que la méthode IBMOLS combinée avec l’indicateur de qualité R2 permet d’obtenir des solutions efficaces et d’intégrer facilement les préférences du décideur. Nous montrons également que dans un contexte où les préférences du décideur sont inconnues ou les objectifs ont tous la même importance, la dominance de Lorenz est un outil très efficace qui permet, d’un côté, d’intégrer l’équité dans le processus de recherche et, d’un autre côté, de réduire le nombre de solutions non dominées ainsi que le temps d’exécution. / Since the early 2000s, the social and medico-social sector is experiencing significant evolutions and mutations. On the one hand, the number of persons taken over is constantly increasing. On the other hand, the finances and budgets available to the structures are constantly decreasing. This forces decision-makers to adapt and find new solutions to do more with fewer resources. In this thesis, we study a practical problem that is often faced by the decision-makers, which is the elaboration of optimal action plans. An action plan is a set of actions that are realized to improve both the performance of the structure and the quality of service offred to its residents. Elaborating an optimal action plan consists of identifying and selecting the best actions that improve all the objectives of the plan while respecting some constraints. After presenting the practical and theoretical context, we provide a formal modeling of the problem as a multi-objective knapsack problem. Then, we present a number of solution methods based on quality indicators and Lorenz dominance. We show that combining IBMOLS method with R2 indicator allows obtaining efficient solutions and easily integrating the decision-maker preferences. We also show that in a context where decision-maker preferences are not known or all the objectives are considered equals, Lorenz dominance is a very efficient tool to incorporate equity into the search process and reduce the number of non-dominated solutions as well as the algorithm runtime.