Development of electric vehicles is motivated by global concerns over the need for environmental protection. In addition to its zero-emission characteristics, an electric propulsion system enables high performance torque control that may be used to maximize vehicle performance obtained from energy-efficient, low rolling resistance tires typically associated with degraded road-holding ability. A simultaneous plant/controller optimization is performed on an electric vehicle traction control system with respect to conflicting energy use and performance objectives. Due to system nonlinearities, an iterative simulation-based optimization approach is proposed using a system model and a genetic algorithm (GA) to guide search space exploration. The system model consists of: a drive cycle with a constant driver torque request and a step change in coefficient of friction, a single-wheel longitudinal vehicle model, a tire model described using the Magic Formula and a constant rolling resistance, and an adhesion gradient fuzzy logic traction controller. Optimization is defined in terms of the all at once variable selection of: either a performance oriented or low rolling resistance tire, the shape of a fuzzy logic controller membership function, and a set of fuzzy logic controller rule base conclusions. A mixed encoding, multi-chromosomal GA is implemented to represent the variables, respectively, as a binary string, a real-valued number, and a novel rule base encoding based on the definition of a partially ordered set (poset) by delta inclusion. Simultaneous optimization results indicate that, under straight-line acceleration and unless energy concerns are completely neglected, low rolling resistance tires should be incorporated in a traction control system design since the energy saving benefits outweigh the associated degradation in road-holding ability. The results also indicate that the proposed novel encoding enables the efficient representation of a fix-sized fuzzy logic rule base within a GA.

Development of electric vehicles is motivated by global concerns over the need for environmental protection. In addition to its zero-emission characteristics, an electric propulsion system enables high performance torque control that may be used to maximize vehicle performance obtained from energy-efficient, low rolling resistance tires typically associated with degraded road-holding ability. A simultaneous plant/controller optimization is performed on an electric vehicle traction control system with respect to conflicting energy use and performance objectives. Due to system nonlinearities, an iterative simulation-based optimization approach is proposed using a system model and a genetic algorithm (GA) to guide search space exploration. The system model consists of: a drive cycle with a constant driver torque request and a step change in coefficient of friction, a single-wheel longitudinal vehicle model, a tire model described using the Magic Formula and a constant rolling resistance, and an adhesion gradient fuzzy logic traction controller. Optimization is defined in terms of the all at once variable selection of: either a performance oriented or low rolling resistance tire, the shape of a fuzzy logic controller membership function, and a set of fuzzy logic controller rule base conclusions. A mixed encoding, multi-chromosomal GA is implemented to represent the variables, respectively, as a binary string, a real-valued number, and a novel rule base encoding based on the definition of a partially ordered set (poset) by delta inclusion. Simultaneous optimization results indicate that, under straight-line acceleration and unless energy concerns are completely neglected, low rolling resistance tires should be incorporated in a traction control system design since the energy saving benefits outweigh the associated degradation in road-holding ability. The results also indicate that the proposed novel encoding enables the efficient representation of a fix-sized fuzzy logic rule base within a GA.

過去的文獻中，資產配置的方法不外乎效率前緣、動態資產配置等方式，但是，單獨針對銀行探討的文章並不多見，所以本文的貢獻在於單獨針對銀行的資產配置行為進行研究，希望能利用『演化策略演算法』，進行『模擬最佳化』來解決銀行資產配置的問題。基本上這個方法是由兩個動作結合而成，先是模擬，再來尋求最佳解。所以，資產面我們選擇了現金、債券、股票、不動產四項標的，而負債面則模擬了定存、活存與借入款這三項業務，然後透過重複執行模型的方式來求出最適解。並與單期資產配置方法下的結果作一比較，發現運用演化策略演算法有較佳的結果，此外，在不同的亂數下，仍具有良好的穩健性，可作為一般銀行經理人參考之用。 / We focus on the bank’s asset allocation problem in this thesis. We use simulation optimization to solve the problem by evolution strategy, which is relatively new in the financial field. Simulation optimization consists of two steps: simulate numerous situations and search for the optimal asset portfolios. In the simulation, we set up four assets, including cash, bond, stock, and real estate and three business lines, including demand deposits, time deposits, and borrowings. Then we search for the optimal solution by running the ES algorithm. The results show that simulation optimization generates better results than one-period asset allocation. Furthermore, the evolution strategy method generates similar results using different random numbers.

La simulation est de plus en plus utilisée dans les études de conception et d’organisation des systèmes complexes. Une étude par optimisation via simulation permet d’optimiser les paramètres d’un système afin d’obtenir les meilleures performances, estimés par la simulation. Toutefois, dans de nombreux systèmes complexes, certaines données sont incertaines (par exemple, les conditions opératoires du système ou le comportement des décideurs). En conséquence, même lorsque l’étude d’optimisation via simulation est réalisée avec le plus grand soin, les solutions obtenues peuvent se révéler inadaptées. Dans ce contexte, notre objectif est d’étudier comment optimiser, via simulation, un système afin qu’il continue d’être performant et robuste. L’étude bibliographique approfondie que nous avons menée montre que très peu de travaux en optimisation via simulation intègrent l’incertain et qu’ils peuvent être très limités dans leur capacité à fournir des solutions robustes en un temps de calcul raisonnable en particulier lorsque des métaheuristiques sont employées. Par ailleurs, la plupart des travaux existants délivrent une solution unique de conception performante du système et ne sont pas adaptés pour prendre en compte les aspects collaboratifs (groupe de décideurs). C’est pourquoi, nous avons proposé une approche originale connectant une recherche des solutions par optimisation évolutionniste multimodale et une évaluation des performances du système via simulation. Notre approche va permettre de fournir plusieurs alternatives performantes de conception d’un système et assez diversifiées pour acquérir aux décideurs une flexibilité dans le choix de la solution à implanter. De plus, nous avons exploité cette flexibilité pour intégrer, d’une part, les préférences individuelles des membres d’une équipe décisionnelle et, d’autre part, la présence de plusieurs environnements pour étudier la robustesse des solutions en un temps de traitement raisonnable par rapport à d’autres approches utilisant des méta heuristiques. Les approches proposées sont illustrées par l’optimisation d’une maille de supply chain. Grâce à cette application, nous avons montré qu’en plus de fournir un choix de solutions performantes pour dimensionner le système, nous pouvons proposer des solutions « collectivement acceptable » pour l’équipe décisionnelle et déterminer des solutions de conception robustes du système. Ces approches fournissent ainsi une flexibilité pour la phase de décision et contribuent à la prise en compte de l’incertitude dans l’optimisation via simulation d’un système. / Simulation is more and more used in studies of design and organization of complex systems. A simulation optimization study search for the system parameters that yield the best performance. However, in many complex systems, data can be uncertain (e.g., the operating conditions of the system or the behavior of decision makers). Therefore, even when the simulation optimization study is performed with the greatest care, the solutions may be inadequate. In this context, our goal is to study how to optimize, via simulation, a robust system. The extensive literature review we conducted shows that few simulation optimization approaches incorporate uncertainty and they can be very limited in their ability to provide robust solutions in a reasonable processing time, especially when metaheuristics are used. In addition, most existing approaches provide a single solution to the design problem and are not adapted to take into account the collaborative aspects (decision maker’s team). Therefore, we propose a novel approach connecting a search for solutions by evolutionary multimodal optimization and the evaluation of the system performance by simulation. Our approach allows to obtain a diverse set of designs that can be considered as efficient in terms of their performance and to provide decision-Makers with flexibility in the choice of the solution to implement. In addition, we use this flexibility to integrate first, the individual preferences of the members of decision maker’s team and secondly, the presence of multiple environments For studying the robustness of solutions in a reasonable processing time compared to other approaches based on metaheuristics. The proposed approaches are illustrated with an example of supply chain. With this application, we have shown that in addition to providing a choice of efficient solutions for sizing the system, we propose "collectively acceptable" solutions to the decision-Making team and we identify robust solutions. Then, these approaches provide flexibility to the decision phase and contribute to the consideration of uncertainty in the simulation optimization of the system.

The Biscayne Aquifer is a primary source of water supply in Southeast Florida. As a coastal aquifer, it is threatened by saltwater intrusion (SWI) when the natural groundwater flow is altered by over-pumping of groundwater. SWI is detrimental to the quality of fresh groundwater sources, making the water unfit for drinking due to mixing and reactions with aquifer minerals. Increasing water demand and complex environmental issues thus force water utilities in South Florida to sustainably manage saltwater intrusion and develop alternative water supplies (e.g., aquifer storage and recovery, ASR). The objectives of this study were to develop and use calibrated geochemical models to estimate water quality changes during saline intrusion and during ASR in south Florida. A batch-reaction model of saltwater intrusion was developed and important geochemical reactions were inferred. Additionally, a reactive transport model was developed to assess fate and transport of major ions and trace metals (Fe, As) at the Kissimmee River ASR. Finally, a cost-effective management of saltwater intrusion that involves using abstraction and recharge wells was implemented and optimized for the case of the Biscayne Aquifer. Major processes in the SWI areas were found to be mixing and dissolution-precipitation reactions with calcite and dolomite. Most of the major ions (Cl, Na, K, Mg, SO4) behaved conservatively during ASR while Ca and alkalinity were affected by carbonate reactions and cation exchange. A complex set of reactions involving thermodynamic equilibrium, kinetics and surface complexation reactions was required in the ASR model to simulate observed concentrations of Fe and As. The saltwater management model aimed at finding optimal locations and flow rates for abstraction and recharge wells. Optimal solutions (i.e., minimum total salt and total cost Pareto front) were produced for the Biscayne Aquifer for scenarios of surface recharge induced by climate change-affected precipitation. In general, abstraction at the maximum rate near the coast and artificial recharge at locations much further inland were found to be optimal. Knowledge developed herein directly supports the understanding of SWI caused by anthropogenic stressors, such as over-pumping and sea level rise, on coastal aquifers.

Orientador: Aneirson Francisco da Silva / Resumo: A Estampagem a Frio é um processo de conformação plástica de chapas metálicas, que possibilita, por meio de ferramentas específicas, obter componentes com boas propriedades mecânicas, geometrias e espessuras variadas, diferentes especificações de materiais e com boa vantagem econômica. A multiplicidade destas variáveis gera a necessidade de utilização de técnicas estatísticas e de simulação numérica, que suportem a sua análise e adequada tomada de decisão na elaboração do projeto das ferramentas de conformação. Este trabalho foi desenvolvido em uma empresa brasileira multinacional de grande porte que atua no setor de autopeças, em seu departamento de engenharia de projetos de ferramentas, com o propósito de reduzir o estiramento e a ocorrência de trincas em uma travessa de 6,8 [mm] de aço LNE 380. A metodologia proposta obtém os valores dos fatores de entrada e sua influência na variável resposta com o uso de técnicas de Delineamento de Experimentos (DOE) e simulação pelo método de Elementos Finitos (FE). Uma Função Empírica é desenvolvida a partir desses dados, com o uso da técnica de regressão, obtendo-se a variável resposta y (espessura na região crítica), em função dos fatores influentes xi do processo. Com a Otimização via Simulação Monte Carlo (OvSMC) insere-se a incerteza nos coeficientes desta Função Empírica, sendo esta a principal contribuição deste trabalho, pois é o que ocorre, por via de regra, na prática com problemas experimentais. Simulando-se por FE as ferram... (Resumo completo, clicar acesso eletrônico abaixo) / Mestre

Die Eisenbahn wird in der Öffentlichkeit als umweltfreundliches Verkehrsmittel gesehen und ist für Personen und Fracht die bedeutendste Alternative zum Straßenverkehr. Die hohe Lärmbelastung, die die Bahn jedoch in Ballungsgebieten oder an stark belasteten Strecken verursacht, führt zu Akzeptanzproblemen in der Bevölkerung und zunehmend in der Politik. Eine Steigerung des Schienenverkehrs ist deshalb nur möglich, wenn die Schallabstrahlung der Schienenfahrzeuge in Zukunft spürbar reduziert werden kann. Im Geschwindigkeitsbereich des konventionellen Güter- und Personenverkehrs ist das Rollgeräusch die dominierende Schallquelle. Bei der Rollbewegung wird die Oberflächenrauheit von Rad und Schiene überfahren und wirkt als Erregung in der Kontaktzone. Rad und Schiene werden in Schwingung versetzt und strahlen Schall ab. Ziel dieser Arbeit ist es, einen ausführbaren CAE-Prozess aufzubauen und anzuwenden, der auf dem aktuellen Stand der Modellierungstechnik die Optimierung von Eisenbahnrädern nach akustischen Gesichtspunkten ermöglicht. Der Kernbestandteil dieses Prozesses sind effiziente Methoden, die es ermöglichen, für einen rotationssymmetrischen Radsatz binnen weniger Sekunden die im Rad-Schiene-System abgestrahlte Schallleistung zu berechnen. Die Modellierung der Schwingung und Schallabstrahlung des rotierenden Radsatzes bildet einen Schwerpunkt. Verschiedene Anregungshypothesen und -modelle werden gegenübergestellt und anhand eines Prüfstandsversuchs auf ihre Validität untersucht. Der Anregungsmechanismus des Rollgeräuschs wird aus der Literatur aufgearbeitet und ein Modell für die Schallvorhersage daraus entwickelt. Dabei spielt die Körperschallleistung des Rades eine entscheidende Rolle. Sie kann mit Hilfe der Ergebnisse einer numerischen Modalanalyse sehr schnell und automatisiert berechnet werden und stellt im Falle des Eisenbahnrades eine effiziente und brauchbare Alternative zu aufwendigen BEM-Simulationen dar. Die Wirkung der Rauheit wird mit einem Kontaktmodell untersucht und die Filterwirkung des Kontakts dabei ermittelt. Es werden Studien zur wegerregten Schwingung im Rad-Schiene-System vorgestellt, in denen sich einige Spezifika offenbaren. Nahe seinen Eigenfrequenzen zeigen sich für den Radsatz erwartungsgemäß erhöhte Schwingungsamplituden. Jedoch ist dies keine eigentliche Resonanz sondern ein Effekt von Antiresonanz bzw. Tilgung. Dies führt u. A. dazu, dass eine Erhöhung der Dämpfung zwar die Schwingung vermindert, die Wirkung jedoch weit hinter der unter Krafterregung zu erwartenden Reduktion zurückbleibt. Ein in ANSYS parametrisch modellierter Güterwagen-Radsatz wird hinsichtlich Masse und Schallleistung optimiert. Es zeigt sich ein Verbesserungspotenzial gegenüber beispielhaft gewählten Referenzradsätzen von ein bis drei Dezibel. Ein für den praktischen Einsatz verwendbares, akustisch optimiertes Rad ist im Rahmen der Arbeit nicht entwickelt worden. Der CAE-Prozess stellt jedoch ein Werkzeug dar, die konstruktiven Freiräume bei der Entwicklung von Radsätzen zielgerichtet so auszunutzen, dass hierbei ein möglichst leises Rad entsteht.

De nos jours, de nombreux systèmes de production sont gérés en flux « tirés » et utilisent des méthodes basées sur des « cartes », comme : Kanban, ConWIP, COBACABANA, etc. Malgré leur simplicité et leur efficacité, ces méthodes ne sont pas adaptées lorsque la production n’est pas stable et que la demande du client varie. Dans de tels cas, les systèmes de production doivent donc adapter la tension de leur flux tout au long du processus de fabrication. Pour ce faire, il faut déterminer comment ajuster dynamiquement le nombre de cartes (ou de ‘e-card’) en fonction du contexte. Malheureusement, ces décisions sont complexes et difficiles à prendre en temps réel. De plus, dans certains cas, changer trop souvent le nombre de cartes kanban peut perturber la production et engendrer un problème de nervosité. Les opportunités offertes par l’industrie 4.0 peuvent être exploitées pour définir des stratégies intelligentes de pilotage de flux permettant d’adapter dynamiquement ce nombre de cartes kanban.Dans cette thèse, nous proposons, dans un premier temps, une approche adaptative basée sur la simulation et l'optimisation multi-objectif, capable de prendre en considération le problème de la nervosité et de décider de manière autonome (ou d'aider les gestionnaires)  quand et où ajouter ou retirer des cartes Kanban. Dans un deuxième temps, nous proposons une nouvelle approche adaptative et intelligente basée sur un réseau de neurones dont l’apprentissage est d’abord réalisé hors ligne à l’aide d’un modèle numérique jumeau (simulation), exploité par une optimisation multi-objectif. Après l’apprentissage, le réseau de neurones permet de décider en temps réel, quand et à quelle étape de fabrication il est pertinent de changer le nombre de cartes kanban. Des comparaisons faites avec les meilleures méthodes publiées dans la littérature montrent de meilleurs résultats avec des changements moins fréquents. / Today, many production systems are managed in "pull" control system and used "card-based" methods such as: Kanban, ConWIP, COBACABANA, etc. Despite their simplicity and efficiency, these methods are not suitable when production is not stable and customer demand varies. In such cases, the production systems must therefore adapt the “tightness” of their production flow throughout the manufacturing process. To do this, we must determine how to dynamically adjust the number of cards (or e-card) depending on the context. Unfortunately, these decisions are complex and difficult to make in real time. In addition, in some cases, changing too often the number of kanban cards can disrupt production and cause a nervousness problem. The opportunities offered by Industry 4.0 can be exploited to define smart flow control strategies to dynamically adapt this number of kanban cards.In this thesis, we propose, firstly, an adaptive approach based on simulation and multi-objective optimization technique, able to take into account the problem of nervousness and to decide autonomously (or to help managers) when and where adding or removing Kanban cards. Then, we propose a new adaptive and intelligent approach based on a neural network whose learning is first realized offline using a twin digital model (simulation) and exploited by a multi-objective optimization method. Then, the neural network could be able to decide in real time, when and at which manufacturing stage it is relevant to change the number of kanban cards. Comparisons made with the best methods published in the literature show better results with less frequent changes.