Submitted by Allison Andrade (allisonandrade.13@hotmail.com) on 2016-03-21T12:59:51Z No. of bitstreams: 1 Dissertação - Raphael Ribeiro Costa.pdf: 1907280 bytes, checksum: f0699239712e00a732544fc8891696a3 (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2016-04-14T14:12:43Z (GMT) No. of bitstreams: 1 Dissertação - Raphael Ribeiro Costa.pdf: 1907280 bytes, checksum: f0699239712e00a732544fc8891696a3 (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2016-04-14T14:15:38Z (GMT) No. of bitstreams: 1 Dissertação - Raphael Ribeiro Costa.pdf: 1907280 bytes, checksum: f0699239712e00a732544fc8891696a3 (MD5) / Made available in DSpace on 2016-04-14T14:15:38Z (GMT). No. of bitstreams: 1 Dissertação - Raphael Ribeiro Costa.pdf: 1907280 bytes, checksum: f0699239712e00a732544fc8891696a3 (MD5) Previous issue date: 2013-01-11 / FAPEAM - Fundação de Amparo à Pesquisa do Estado do Amazonas / This paper presents a characterization of solutions (efficient or pareto-optimal) for multiobjective optimization problems based on the calculation of sets tangent the theoretical discuss some elements of convex analysis, cones theory as well as multiobjective optimization elements needed for the formulation of the model. Furthermore, some classic multiobjectives methods are presented and classified into three types: priori, posteriori and interactive. From these elements, makes up the optimality conditions for general multiobjective problems, using sets of approximation of all of their issue viable set of the respective problem. At the end, the feasible set of the problem is defined by equality and inequality constraints and, with skill and regularity conditions, we obtain the Lagrange multipliers. / Neste trabalho apresenta-se uma caracterização das soluções (eficientes ou pareto-ótimo) para problemas de otimização multiobjetivo baseado no cálculo de conjuntos tangentes. Os fundamentos teóricos discorrem sobre alguns elementos de análise convexa, teoria dos cones bem como elementos de otimização multiobjetivo necessários para formulação do modelo. Além disso, apresentam-se alguns métodos multiobjetivos clássicos que são classificados em três tipos: à priori, à posteriori e interativos. A partir destes elementos, formula-se as condições de otimalidade para problemas multiobjetivos gerais usando conjuntos de aproximação do conjunto viável do respectivo problema. Ao final, o conjunto viável do problema é definido por restrições de igualdade e desigualdade e, com condições de qualificação e regularidade, obtém-se os multiplicadores de Lagrange.

A teoria dos valores extremos é um tópico da probabilidade que descreve a distribuição assintótica das estatísticas de ordem, tais como máximos ou mínimos, de uma seqüência de variáveis aleatórias que seguem uma função de distribuição F normalmente desconhecida. Descreve, ainda, a distribuição assintótica dos excessos acima de um valor limiar de um ou mais termos dessa seqüência. Dessa forma, as metodologias padrões utilizada neste contexto consistem no ajuste da distribuição generalizada dos valores extremos a uma série de máximos anuais ou no ajuste da distribuição generalizada de Pareto a uma série de dados compostas somente de observações excedentes de um valor limiar. No entanto, segundo Coles et al. (2003), há uma crescente insatisfação com o desempenho destes modelos padrões para predição de eventos extremos causada, possivelmente, por pressuposições não atendidas como a de independência das observações ou pelo fato de que os mesmos não sejam recomendados para serem utilizados em algumas situações específicas como por exemplo e quando observações de máximos anuais compostas por duas ou mais populações independentes de eventos extremos sendo que a primeira descreve eventos menos freqüentes e de maior magnitude e a segunda descreve eventos mais freqüentes e de menor magnitude. Então, os dois artigos que compõem este trabalho tem como objetivo apresentar alternativas de análise de valores extremos para estas situações em que o ajuste dos modelos padrões não são adequados. No primeiro, foram ajustadas as distribuições generalizada de Pareto e exponencial, caso particular da GP, aos dados de vazão média diária do Posto de Artemis, Piracicaba, SP, Brasil, conjuntamente com a técnica do desagrupamento, (declustering), e comparadas as estimativas dos níveis de retorno para períodos de 5, 10, 50 e 100 anos. Conclui-se que as estimativas intervalares dos níveis de retorno obtidas por meio do ajuste da distribuição exponencial são mais precisas do que as obtidas com o ajuste da distribuição generalizada de Pareto. No segundo artigo, por sua vez, foi apresentada uma metodologia para o ajuste da distribuição de Gumbel e de misturas de duas distribuições de Gumbel aos dados de velocidades de ventos mensais de Piracicaba, SP. Selecionou-se a distribuição que melhor ajustou-se aos dados por meio de testes de hipóteses bootstrap paramétrico e critérios de seleção AIC e BIC. E concluiu-se que a mistura de duas distribuições de Gumbel é a distribuição que melhor se ajustou-se aos dados de velocidades máxima de ventos dos meses de abril e maio, enquanto que o ajuste da distribuição de Gumbel foi o melhor para os meses de agosto e setembro. / The extreme value theory is a probability topics that describes the asymtoptic distribution of order statistics such as maximum or minimum of random variables sequence that follow a distribution function F normaly unknown. Describes still, the excess asymtoptic distribution over threshold of this sequence. So, the standard methodologies of extremes values analysis are the fitting of generalized extreme value distribution to yearly maximum series or the fitting of generalized Pareto distribution to partial duration series. However, according to Coles et al. (2003), there is a growing dissatisfaction with the use this standard models for the prediction of extremes events and one of possible causes this fact may be a false assumptions about a sequence of observed data as a independence assumptions or because the standards models must not used in some specific situations like for example when maximum sample arise from two or more independents populations, where the first population describes more frequents and low intense events and the second population describes less frequents and more intense events. In this way, the two articles this work has a objective show alternatives about extreme values analysis for this situations that the standards models doesn´t recommended. In the first article, the generalized distribution Pareto and exponencial distribution, particular case of GP, together with to declustering methods was applied to mean daily flow of the Piracicaba river, Artemis station, Piracicaba, SP, and the estimates the return levels of 5, 10, 50 and 100 years were compared. We conclude that the interval estimates of the 50 and 100 year return levels obtained using the fitting the exponencial distribution are more precise than those obtained using the generalized Pareto distribution. In the second article, we propose the fit of Gumbel distribution and the Gumbel mixture to data maximum speed wind in Piracicaba, SP. We select the best model using bootstrap test of hypotheses and the AIC and BIC selection criteria We conclude that the mixture Gumbel is the best model to analyze the maximum wind speed data for months of april e may and otherside the fit of Gumbel distributions was the best fit to months of august e september.

The shipping industry is very large and ships require a substantial amount of fuel. However, fuel consumption is not the only concern. Time of arrival, safety concerns, distance travelled etc. are also of importance and these objectives might be inherently conflicting. This thesis aims to demonstrate multi-objective optimization and Pareto navigation for application in voyage planning. In order to perform this optimization, models of weather, ocean conditions, ship dynamics and propulsion system are needed. Statistical methods for estimation of resistance experienced in calm and rough sea are used. An earlier developed framework is adopted to perform the optimization and Pareto navigation. The results show that it is a suitable approach in voyage planning. A strength of the interactive Pareto navigation is the overview of the solution space presented to the decision maker and the control of the spread of the objective space. Another benefit is the possibilities of assigning specific values on objectives and setting thresholds in order to narrow down the solution space. The numerical results reinforces the trend of slow steaming to decrease fuel consumption.

In this work, the potential of intelligent algorithms for optimizing the real power loss and enhancing the grid connection power factor in a real hydrocarbon facility electrical system is assessed. Namely, genetic algorithm (GA), improve strength Pareto evolutionary algorithm (SPEA2) and differential evolutionary algorithm (DEA) are developed and implemented. The economic impact associated with these objectives optimization is highlighted. The optimization of the subject objectives is addressed as single and multi-objective constrained nonlinear problems. Different generation modes and system injected reactive power cases are evaluated. The studied electrical system constraints and parameters are all real values. The uniqueness of this thesis is that none of the previous literature studies addressed the technical and economic impacts of optimizing the aforementioned objectives for real hydrocarbon facility electrical system. All the economic analyses in this thesis are performed based on real subsidized cost of energy for the kingdom of Saudi Arabia. The obtained results demonstrate the high potential of optimizing the studied system objectives and enhancing the economics of the utilized generation fuel via the application of intelligent algorithms.

Tropical Cyclones are a continuing threat to life and property. Willoughby (2012) found that a Pareto (power-law) cumulative distribution fitted to the most damaging 10% of US hurricane seasons fit their impacts well. Here, we find that damage follows a Pareto distribution because the assets at hazard follow a Zipf distribution, which can be thought of as a Pareto distribution with exponent 1. The Z-CAT model is an idealized hurricane catastrophe model that represents a coastline where populated places with Zipf- distributed assets are randomly scattered and damaged by virtual hurricanes with sizes and intensities generated through a Monte-Carlo process. Results produce realistic Pareto exponents. The ability of the Z-CAT model to simulate different climate scenarios allowed testing of sensitivities to Maximum Potential Intensity, landfall rates and building structure vulnerability. The Z-CAT model results demonstrate that a statistical significant difference in damage is found when only changes in the parameters create a doubling of damage.

The purpose of this research project is the design and optimization of complex chemical engineering problems, by employing evolutionary algorithms (EAs). EAs are optimization techniques which mimic the principles of genetics and natural selection. Given their population-based approach, EAs are well suited for solving multiobjective optimization problems (MOOPs) to determine Pareto-optimal solutions. The Pareto front refers to the set of non-dominated solutions which highlight trade-offs among the different objectives. A broad range of applications have been studied, all of which are drawn from the chemical engineering field. The design of an industrial packed bed styrene reactor is initially studied with the goal of maximizing the productivity, yield and selectivity of styrene. The dual population evolutionary algorithm (DPEA) was used to circumscribe the Pareto domain of two and three objective optimization case studies for three different configurations of the reactor: adiabatic, steam-injected and isothermal. The Pareto domains were then ranked using the net flow method (NFM), a ranking algorithm that incorporates the knowledge and preferences of an expert into the optimization routine. Next, a multiobjective optimization of the heat transfer area and pumping power of a shell-and-tube heat exchanger is considered to provide the designer with multiple Pareto-optimal solutions which capture the trade-off between the two objectives. The optimization was performed using the fast and elitist non-dominated sorting genetic algorithm (NSGA-II) on two case studies from the open literature. The algorithm was also used to determine the impact of using discrete standard values of the tube length, diameter and thickness rather than using continuous values to obtain the optimal heat transfer area and pumping power. In addition, a new hybrid algorithm called the FP-NSGA-II, is developed in this thesis by combining a front prediction algorithm with the fast and elitist non-dominated sorting genetic algorithm-II (NSGA-II). Due to the significant computational time of evaluating objective functions in real life engineering problems, the aim of this hybrid approach is to better approximate the Pareto front of difficult constrained and unconstrained problems while keeping the computational cost similar to NSGA-II. The new algorithm is tested on benchmark problems from the literature and on a heat exchanger network problem.

This master thesis contains three independent papers on the Zip's law for cities. In the first essay I summarize accumulated knowledge and use examples from the Czech Republic to show problems of the empirical research. The main findings of this essay are: City size distribution in the Czech Republic can be better described by a log-normal distribution than by a Pareto distribution; Pareto exponents are sensitive to sample selection. The second essay is the largest empirical cross-country study on Zipf's law for cites. The mean value for 157 countries is 0.919. The comparison with the study by Soo (2005) showed a decreasing tendency of the Pareto exponent, since for the same countries, the average exponent decreased from 1.11 to 1.02. One possible explanation of this trend is the process of urbanization. The last essay looks at the topic from a different angle. I have developed an agent based model to describe the process of suburbanization and cities merging and its impact on the size of the Pareto exponent. I have shown that when cities merge, the exponent starts to fall down from a steady state.

<p>Multi-criteria decision making (MCDM) methods have evolved over the past several decades. In today’s world with rapidly growing industries, MCDM has proven to be significant in many application areas. In this study, a decision-making model is devised using reinforcement learning to carry out multi-criteria optimization problems. Learning automata algorithm is used to identify an optimal solution in the presence of single and multiple environments (criteria) using pareto optimality. The application of this model is also discussed, where the model provides an optimal solution to the food, energy, and water systems (FEWS) problem.</p>

The diploma thesis focuses on streamlining sales of choosen retail using Pareto analysis. Theoretical part specifies using Pareto analysis in academic thesis and mentions example of its application. The merit of this thesis is an invention of a methodology that could be used for evaluation of the assortment. This methodology helps to identify the most profitable goods which are sold in the highest numbers and thus bring the highest profit to the retail. On base of performed analysis is presented suggestion of streamlining assortment.

In this thesis I dealt with the Six Sigma method and uses in practice in Miele technica s.r.o. company. The first part deals with the theoretical description of Six Sigma, DMAIC methodology and used tools as SIPOC, Ishikawa diagram, Pareto diagram, or regression or correlation. The second part describes the application of Six Sigma methods, which I had the opportunity to perform as a team leader in the assembly process of washing machines and dryers.