Esta Dissertação aborda o Problema de Alocação de Aulas a Salas (PAAS), também conhecido como Problema de Alocação de Salas (PAS). As instituições de ensino superior, no começo de seus calendários letivos, resolvem um PAAS ao determinar os espaços a serem utilizados para as atividades didáticas. Porém, em muitas destas instituições o PAAS é ainda resolvido manualmente, gerando altas cargas de trabalho para os responsáveis. Neste trabalho, o Instituto de Ciências Matemáticas e de Computação (ICMC) da Universidade de São Paulo (USP) foi tomado como caso de estudo para o PAAS. Um modelo de programação matemática inteiro é proposto e abordado por técnicas de resolução exata, metaheurísticas mono-objetivo e uma abordagem multi-objetivo. Uma estrutura de vizinhança proposta obteve resultados comparáveis à da metodologia exata, para um tempo fixo de execução. Demonstra-se que, a abordagem multi-objetivo é uma possibilidade de contornar algumas dificuldades clássicas do problema, como incertezas sobre a escolha dos pesos das métricas. Os métodos de solução propostos para o problema fornecem, aos responsáveis, bons instrumentos de auxílio à tomada de decisão para o PAAS. / This Dissertation addresses the Classroom Assignment Problem (CAP). All Higher Education Institutes, at the schoolyear\'s begin, faces a CAP to define where the classes will be taught. However, many of those still solves this problem manually, demanding high efforts from the responsible staff. In this study, the Universidade de São Paulo\'s (USP) Instituto de Ciências Matemáticas e de Computação (ICMC) was tackled as study case for the CAP. An Integer Programming Model is proposed and tackled by exact methods, meta-heuristics and a multi-objective approach. A novel neighborhood operator is proposed for the local search and obtains good results, even comparable to the exact method. The multi-objective approach is shown to overcome some of the classical adversity of the mono-objective approach, e.g., choosing weights to quality metric. Those CAP\'s proposed solution methods, gives the responsible staff a good decision making support.

Les échangeurs de chaleur sont utilisés dans de nombreux secteurs industriels. L'optimisation de leurs performances est donc de première importance pour réduire la consommation énergétique. Le comportement d'un échangeur est intrinsèquement multiéchelle : l'échelle locale de l'intensification des phénomènes de transfert thermique côtoie une échelle plus globale où interviennent des phénomènes de distribution de débit. Un échangeur de chaleur est également le siège de différents phénomènes physiques, tels que la mécanique des fluides, la thermique et l'encrassement. Les présents travaux proposent une méthode d'optimisation multiobjectif de la forme des échangeurs, robuste, pouvant traiter les aspects multiéchelles et multiphysiques et applicable dans un contexte industriel. Les performances de l'échangeur sont évaluées par des simulations de mécanique des fluides numérique (CFD) et par des méthodes globales (є-NUT). Suite à une étude bibliographique, une méthode de métamodélisation par krigeage associée à un algorithme génétique ont été retenus. Des méthodes de visualisation adaptées (clustering et Self-Organizing Maps) sont utilisées pour analyser les résultats. Le métamodèle permet d'approcher la réponse d'un simulateur (CFD) et d'en fournir une prédiction dont l'interrogation est peu onéreuse. Le krigeage permet de prendre en compte une discontinuité et des perturbations de la réponse du simulateur par l'ajout d'un effet de pépite. Il permet également l'utilisation de stratégies d'enrichissement construisant des approximations précises à moindre coût. Cette méthode est appliquée à différentes configurations représentatives du comportement de l'échangeur, permettant de s'assurer de sa robustesse lorsque le simulateur change, lorsque l'aspect multiéchelle est pris en compte ou lorsque une physique d'encrassement est considérée. Il a été établi que l'étape de métamodélisation assure la robustesse de la méthode et l'intégration de l'aspect multiéchelle. Elle permet aussi de construire des corrélations à l'échelle locale qui sont ensuite utilisées pour déterminer les performances globales de l'échangeur. Dans un contexte industriel, les méthodes d'analyse permettent de mettre en évidence un nombre fini de formes réalisant un compromis des fonctions objectif antagonistes. / Heat exchangers are used in many industrial applications. Optimizing their performances is a key point to improve energy efficiency. Heat exchanger behaviour is a multi-scale issue where local scale enhancement mechanisms coexist with global scale distribution ones. It is also multi-physics such as fluid mecanics, heat transfer and fouling phenomenons appear. The present work deals with multi-objective shape optimization of heat echanger. The proposed method is sufficiently robust to address multi-scale and multi-physics issues and allows industrial applications. Heat exchanger performances are evaluated using computational fluid dynamics (CFD) simulations and global methods (є-NUT). The optimization tools are a genetic algorithm coupled with kriging-based metamodelling. Clustering and Self-Organizing Maps (SOM) are used to analyse the optimization results. A metamodel builts an approximation of a simulator response (CFD) whose evaluation cost is reduced to be used with the genetic algorithm. Kriging can address discontinuities or perturbations of the response by introducing a nugget effect. Adaptive sampling is used to built cheap and precise approximation. The present optimization method is applied to different configurations which are representative of the heat exchanger behaviour for both multi-scale and multi-physics (fouling) aspects. Results show that metamodelling is a key point of the method, ensuring the robustness and the versatility of the optimisation process. Also, it allows to built correlations of the local scale used to determine the global performances of the heat exchanger. Clustering and SOM highlight a finite number of shapes, which represent a compromise between antagonist objective functions, directly usable in an industrial context.

The indiscriminate emission of CO2 is drastically aggravating climate change. Carbon Capture and Utilization (CCU) was born as a complementary solution to this issue. This thesis studies the consumption of carbon dioxide in industrial processes, starting from synthesis gas, and using this building block in subsequent syntheses; ultimately integrating CO2 utilization with previously non-CO2 consuming processes.

Spotřeba a energetická efektivita se stává jedním z nejdůležitějších parametrů při návrhu počítačových systémů, zejména kvůli omezené kapacitě napájení u zařízení napájených bateriemi a velmi vysoké spotřebě energie rostoucích datacenter a cloudové infrastruktury. Současně jsou uživatelé ochotni do určité míry tolerovat nepřesné nebo chybné výpočty v roustoucím počtu aplikací díky nedokonalostem lidských smyslů, statistické povaze výpočtů, šumu ve vstupních datech apod. Přibližné počítání, nová oblast výzkumu v počítačovém inženýrství, využívá rozvolnění požadavků na funkčnost za účelem zvýšení efektivity počítačových systémů, pokud jde o spotřebu energie, výpočetní výkon či složitost. Aplikace tolerující chyby mohou být implementovány efektivněji a stále sloužit svému účelu se stejnou nebo mírně sníženou kvalitou. Ačkoli se objevují nové metody pro návrh přibližně počítajících výpočetních systémů, je stále nedostatek automatických návrhových metod, které by nabízely velké množství kompromisních řešení dané úlohy. Konvenční metody navíc často produkují řešení, která jsou daleko od optima. Evoluční algoritmy sice přinášejí inovativní řešení složitých optimalizačních a návrhových problémů, nicméně trpí několika nedostatky, např. nízkou škálovatelností či vysokým počtem generací nutných k dosažení konkurenceschopných výsledků. Pro přibližné počítání je vhodný zejména multikriteriální návrh, což existující metody většinou nepodporují. V této práci je představen nový automatický multikriteriální paralelní evoluční algoritmus pro návrh a aproximaci digitálních obvodů. Metoda je založena na kartézském genetickém programování, pro zvýšení škálovatelnosti byla navržena nová vysoce paralelizovaná implementace. Multikriteriální návrh byl založen na principech algoritmu NSGA-II. Výkonnost implementace byla vyhodnocena na několika různých úlohách, konkrétně při návrhu (přibližně počítajících) aritmetických obvodů, Booleovských funkcích s vysokou nelinearitou či přibližných logických obvodů pro tří-modulovou redundanci. V těchto úlohách bylo dosaženo význammých zlepšení ve srovnání se současnými metodami.

Tato dizertační práce pojednává o více-kriteriálních optimalizačních algoritmech s proměnným počtem dimenzí. Takový algoritmus umožňuje řešit optimalizační úlohy, které jsou jinak řešitelné jen s použitím nepřirozených zjednodušení. Výzkum optimalizačních method s proměnnou dimenzí si vyžádal vytvoření nového optimalizačního frameworku, který obsahuje vedle zmíněných vícekriteriálních metod s proměnnou dimenzí – VND-GDE3 a VND-MOPSO – i další optimalizační metody různých tříd. Optimalizační framework obsahuje také knihovnu rozličných testovacích problémů. Mezi nimi je také sada více-kriteriálních testovacích problémů s proměnnou dimenzí, které byly navrženy pro nastavení a ověření nových metod s proměnnou dimenzí. Nové metody jsou dále použity k optimalizaci několika různorodých optimalizačních úloh z reálného světa.

This paper deals with multi-objective optimization problems (MOOP). It is explained, what solutions in multi-objetive search space are optimal and how are optimal (non-dominated) solutions found in the set of feasible solutions. Afterwards, principles of NSGA-II, MOPSO and GDE3 algorithms are described. In the following chapters, benchmark metrics and problems are introduced. In the last part of this paper, all the three algorithms are compared based on several benchmark metrics.

<p>Presently, internal combustion engines provide power to move the majority of vehicles on the roadway. While battery-powered electric vehicles provide an alternative, their widespread acceptance is hindered by range anxiety and longer charging/refueling times. Dynamic wireless power transfer (DWPT) has been proposed as a means to reduce both range anxiety and charging/refueling times. In DWPT, power is provided to a vehicle in motion using electromagnetic fields transmitted by a transmitter embedded within the roadway to a receiver at the underside of the vehicle. For commercial vehicles, DWPT often requires transferring hundreds of kW through a relatively large airgap (> 20 cm). This requires a high-power DC-AC converter at the transmitting end and a DC-AC converter within the vehicle. </p> In this research, a focus is on the development of models that can be used to support the design of DWPT systems. These include finite element-based models of the transmitter/receiver that are used to predict power transfer, coil loss, and core loss in DWPT systems. The transmitter/receiver models are coupled to behavioral models of power electronic converters to predict converter efficiency, mass, and volume based upon switching frequency, transmitter/receiver currents, and source voltage. To date, these models have been used to explore alternative designs for a DWPT intended to power Class 8-9 vehicles on IN interstates. Specifically, the models have been embedded within a genetic algorithm-based multi-objective optimization in which the objectives include minimizing system mass and minimizing loss. Several designs from the optimization are evaluated to consider practicality of the proposed designs.

Im bisherigen Auslegungsprozess wird ein Bahnsystem in der Regel in Teilsysteme zerlegt, die nacheinander und für sich betrachtet entworfen werden. Das Verhalten des Gesamtsystems im geplanten täglichen Betrieb wird nur für wenige Varianten mittels Simulation überprüft. In dieser Arbeit wird der Ansatz vorgestellt, ein elektrisches Bahnsystem als Optimierungsaufgabe zu modellieren und diese mit einem geeigneten mathematischen Suchverfahren zu lösen, um Wechselwirkungen im Gesamtsystem bereits während der Dimensionierung berücksichtigen zu können. Zu diesem Zweck wird ein mehrkriterieller genetischer Algorithmus mit Zugfahrtsimulation und Netzberechnung gekoppelt, um ein für elektrische Bahnen entwickeltes Optimierungsmodell zu lösen. Am Beispiel einer realen Metrostrecke wird das Verfahren auf seine Eignung getestet und die erzielten Ergebnisse bewertet. / In the previous design process the electric railway system was subdivided into subsystems that are conceived one after the other and independent of each other. The performance of the complete railway system under realistic operation conditions can only be verified for some very few variants using simulation tools. The paper presents an approach to formulate an electric railway system as a self-contained optimization problem solved by means of a mathematical optimization method in order to consider interactions within the system in the early stage of the design process. Therefore a multi-objective genetic algorithm is coupled with both train simulation and electrical network calculation solving an optimization model specially designed for electrical railway systems. The proposed method is tested on an actual metro system. The results of this case study are presented and evaluated.

Getinge AB is a global medical technology company. This master’s thesis is based on the outflow of capital equipments from Getinge’s factory in Växjö to four different sales and service units. The purpose of this thesis is to give Getinge a deeper insight of why the customers and the own organization do not know when they can expect their products. This makes most requests urgent and thus prohibits them from using the best environmental and cost efficient modes of transportation. Two sub-problems have been created in order to investigate this. Sub-problem 1 originates from an organizational perspective. The aim of this problem is to examine the possibilities to achieve less urgent transportations by improving the communication between sales and service units, factories and logistics services. This is evaluated based on semi-structured interviews containing both qualitative and quantitative questions with employees rep- resenting the different functions at the company. It appeared that different phrases, explaining the same thing, were used internally leading to confu- sion. Further, the different functions have harmonized follow-up sessions but do not share the information between each other. The resulting information vacuum creates trust issues and unnecessary time margins and buffers. Sub-problem 2 concerns the trade-off between the economic and environmen- tal impacts in relation to the Greenhouse Gas Protocol Scope 3. This trade- off is evaluated by a multi-objective optimization model, where emissions are priced based on the EU ETS market valuation. Current research argues that the choice of transportation mode is the simplest emissions abatement option in terms of implementation. This study indicates that it is possible for Getinge, in the short-term, to decrease costs and emissions by just chang- ing between current transportation modes. However, a long-term strategy should include evaluation of consolidations, alternative fuels and electrified vehicles since the cost of decreasing one kilogram of emissions by changing between current transportation modes will increase. Finally, increased transparency and communication between sales and ser- vice units, factory and logistics services could be achieved via a one point of contact solution. This could avoid unnecessary time margins and buffers and hence open up the possibility of better over all lead time utilization. This could make it easier to use more environmental friendly transportation modes and thus lower emissions and costs, while still satisfying the customers.

The aim of this master thesis is to study algorithms for automatically tuning antenna parameters to improve the performance of the radio access part of a telecommunication network and user experience. There are four dierent optimization algorithms, Stepwise Minimization Algorithm, Random Search Algorithm, Modied Steepest Descent Algorithm and Multi-Objective Genetic Algorithm to be applied to a model of a radio access network. The performances of all algorithms will be evaluated in this thesis. Moreover, a graphical user interface which is developed to facilitate the antenna tuning simulations will also be presented in the appendix of the report.