Orientador: Paulo de Barros Correia / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-27T01:59:39Z (GMT). No. of bitstreams: 1 Silva_ElisaBastos_D.pdf: 2776184 bytes, checksum: 20b2252b72c7204d062893f8dcb3d304 (MD5) Previous issue date: 2015 / Resumo: Leilões de novos empreendimentos de energia envolvem o compromisso de construí-los e o direito de explorá-los por meio de contratos de outorga. O leiloeiro, cujo objetivo é minimizar o pagamento pela energia contratada, buscando a redução de seu preço para os consumidores finais, fornece o direito de outorga da usina para o vencedor. O licitante é um investidor, e.g., uma empresa de geração que procura maximizar seu benefício com a venda de energia proveniente do empreendimento. Quando a natureza desses empreendimentos é complementar, torna-se possível proporcionar maiores benefícios aos licitantes, e maior eficiência ao leilão, caso sejam negociados em conjunto. Atualmente, o projeto de leilão instituído é composto por uma abordagem híbrida, sequencial e simultânea, que não permite a extração das sinergias entre empreendimentos. Esta tese examina duas metodologias híbridas de leilões reversos, considerando-se o ponto de vista do leiloeiro. O primeiro modelo, centralizado, é composto por duas fases: uma simultânea de lance aberto e outra combinatória de lance fechado. A fase simultânea incentiva a revelação do preço da energia, enquanto a fase combinatória oferece oportunidade aos licitantes de submeterem ofertas mais agressivas através de pacotes de empreendimentos complementares. O modelo centralizado é formulado como um problema de otimização inteiro e combinatório. A função-objetivo consiste em minimizar o pagamento, isso é, energia multiplicada pelo preço (lance) para todas as usinas. A estratégia de solução identifica os vencedores, resolvendo um problema de set-packing restrito. A segunda metodologia utiliza uma abordagem, também, em duas fases. A primeira é um projeto simultâneo de lance aberto, e a segunda fase um projeto combinatório descentralizado. Nesse modelo, a dificuldade do problema aumenta progressivamente à medida que os pacotes são ofertados. A dificuldade da alocação é distribuída entre os licitantes e, por isso, o leiloeiro não necessita resolver um problema de otimização. As metodologias propostas são aplicadas aos leilões de energia nova para o setor elétrico brasileiro. Os resultados mostram que a utilização de ambas as metodologias resolvem o problema de alocação com um tempo computacional aceitável / Abstract: Auctions for new power plants involve a commitment of constructing and the right of exploring them through power sales contracts. The auctioneer -- whose objective is to minimize the payment for the contracted energy, seeking to reduce prices for consumers -- provides the power plant's right for the winner. The bidder is an investor, for example, a generation company, which aims to maximize benefits of energy sales. When the power plant's nature is complementary, it is possible to provide more benefits to bidders and greater efficiency to the auction if these plants were traded together. Currently, the instituted auction design consists of a hybrid approach -- sequential and simultaneous -- which does not allow the extraction of synergies among plants. This thesis examines two hybrid methods of reverse auctions from the auctioneer's view point. The first model, centralized, consists of two phases: a simultaneous open bid and a combinatorial sealed bid. The simultaneous phase encourages the energy prices revelation. The combinatorial phase allows aggressive bidders to acquire bundles of complementary plants. The centralized model is formulated as an integer and combinatorial optimization problem. The objective function consists of minimizing the payment, that is, energy multiplied by the price (bid) for all plants. The solution strategy identifies the winners solving a restricted set-packing problem. The second method also uses a two phase approach. The first phase is a simultaneous open bid design and the second phase is a decentralized combinatorial design. In this model, the problem difficulty increases gradually. The allocation difficulty is distributed among the bidders; therefore, the auctioneer does not need to solve an optimization problem. The proposed methodologies are applied to new energy auctions on Brazilian electrical energy sector. The results show the use of both methods solving the problem of allocation with an acceptable computational time / Doutorado / Planejamento de Sistemas Energeticos / Doutora em Planejamento de Sistemas Energéticos

This thesis deals with modelling diffenrent types of production lines. Modeling is done by the mathematical programming and simulation methods. Optimization related computations are mostly implemented in program GAMS. Simulation is realized by using program Matlab/SimEvents. The results are presented by the Gantt diagrams.

The thesis deals with possibilities of mathematical modeling for public university budgets. Firstly, external conditions of public university financial inflows are discussed and~illustrated by using particular data. The related basic legislature is introduced. In~the~next part, internal financial flows are described the help of a general scheme. Step-by-step, a mathematical model of the university budget was built with using analysis of existing data, rules and formulas. The proposed model represents nonlinear multi–stage scenario-based stochastic programme, involving linear and network-flow like constraints. It allows to take into account more objective functions and related parametric analysis. The model was implemented in the algebraic modeling system GAMS with~interface to MS Excel. The aim of the presented mathematical model was not to offer a tool that would be used for automatic distribution of financial resources of the university, but to give flexible possibilities to its user to realize computational experiments and in this way to achieve a deeper insight into the modeled problem.

This study described an alternative approach for flotation circuit optimization using a mathematical programming technique. Mathematical formulation resulted in mixed integer nonlinear programming problem. Experimental method was used to determine operating conditions of flotation circuit such as flotation circuit stream grades. These conditions were used as the basis for solving optimization problem formulated. The results of the optimization problem were obtaining by setting up the problem in MATLAB optimization toolbox. Performance of flotation circuit in terms of recovery with respect to operating conditions such as residence, number of cells and rate constant has been presented. Stage recoveries were presented as well as overall recovery of the entire flotation circuit. Optimization strategy used superstructure to compare and analyse different alternatives flotation circuits configurations on the basis of stage recoveries. Five circuit alternatives were evaluated are best performing were identified. The statistical analysis was carried out using Statistical Package for Social Sciences (SPSS) software for analysing data derived from mathematical formulation developed for three stages of flotation circuit. Statistically, alternatives A and B can be considered as the most efficient alternatives for the Rougher recovery since they have the same highest means relative to others. Alternative B has the highest mean of 0.995 followed by Alternative A with a mean of 0.991, the least being alternatives D, C and E, respectively. These results imply that Alternative B could be the most efficient alternatives for overall circuit recovery against all other alternatives. One of the key findings were that recovery rate at the rougher stage is higher than the one at the cleaner stage. This results also showed flotation circuits with recycle streams yield comparatively good performance in terms of recovery at rougher stage as compared to circuit without recycle stream. / Civil and Chemical Engineering / M. Tech. (Chemical Engineering)

No description available.

Cybersecurity has become an increasingly important concern for both military and civilian infrastructure globally. Because of the complexity that comes with wireless networks, adversaries have many means of infiltration and disruption of wireless networks. While there is much research done in defending these networks, understanding the robustness of these networks is tantamount for both designing new networks and examining possible security deficiencies in preexisting networks. This dissertation proposes to examine the robustness of wireless networks on three major fronts: the physical layer, the data-link layer, and the network layer. At the physical layer, denial-of-service jamming attacks are considered, and both additive interference and no interference are modeled in an optimal configuration and five common network topologies. At the data-link layer, data transmission efficacy and denial-of-sleep attacks are considered with the goal of maximizing throughput under a constrained lifetime. At the network layer, valid and anomalous communications are considered with the goal of classifying those anomalous communications apart from valid ones. This dissertation proposes that a thorough analysis of the aforementioned three layers provides valuable insights to robustness on general wireless networks.

This research is concerned with the development of algorithmic approaches for solving problems that arise in the design and analysis of telecommunication networks, location-allocation distribution contexts, and intelligent transportation networks. Specifically, the corresponding problems addressed in these areas are a local access and transport area (LATA) network design problem, the discrete equal-capacity p-median problem (PMED), and the estimation of dynamic origin-destination path ows or trip tables in a general network. For the LATA network problem, we develop a model and apply the Reformulation-Linearization Technique (RLT) to construct various enhanced tightened versions of the proposed model. We also design efficient Lagrangian dual schemes for solving the linear programming relaxation of the various enhanced models, and construct an effective heuristic procedure for deriving good quality solutions in this process. Extensive computational results are provided to demonstrate the progressive tightness resulting from the enhanced formulations and their effect on providing good quality feasible solutions. The results indicate that the proposed procedures typically yield solutions having an optimality gap of less than 2% with respect to the derived lower bound, within a reasonable effort that involves the solution of a single linear program. For the discrete equal-capacity p-median problem, we develop various valid inequalities, a separation routine for generating cutting planes via specific members of such inequalities, as well as an enhanced reformulation that constructs a partial convex hull representation that subsumes an entire class of valid inequalities via its linear programming relaxation. We also propose suitable heuristic schemes for solving this problem, based on sequentially rounding the continuous relaxation solutions obtained for the various equivalent formulations of the problem. Extensive computational results are provided to demonstrate the effectiveness of the proposed valid inequalities, enhanced formulations, and heuristic schemes. The results indicate that the proposed schemes for tightening the underlying relaxations play a significant role in enhancing the performance of both exact and heuristic solution methods for solving this class of problems. For the estimation of dynamic path ows in a general network, we propose a parametric optimization approach to estimate time-dependent path ows, or origin-destination trip tables, using available data on link traffic volumes for a general road network. Our model assumes knowledge of certain time-dependent link ow contribution factors that are a dynamic generalization of the path-link incidence matrix for the static case. We propose a column generation approach that uses a sequence of dynamic shortest path subproblems in order to solve this problem. Computational results are presented on several variants of two sample test networks from the literature. These results indicate the viability of the proposed approach for use in an on-line mode in practice. Finally, we present a summary of our developments and results, and offer several related recommendations for future research. / Ph. D.

No description available.

For as long as managed mutual funds have been in existence there has been a desire to accurately assess their relative performance against each other, and also their respective performance in relation to an appropriate stock market index. There has been a specific interest in whether the expensive, professionally managed mutual funds can justify their high cost with respect to low cost, simple index trackers by producing superior, post-cost performance, and this proposition is implicitly tested within this thesis. The aim of this thesis is to undertake an empirical assessment of the managerial performance of mutual funds utilising a three-stage DEA-SFA-DEA methodology which combines linear mathematical programming (DEA) and stochastic frontier analysis (SFA). Specifically, this thesis focuses on evaluating the managerial performance of UK domiciled open-ended investment companies (OEICs) and unit trusts (UTs) over a three year period from 1st January 2008 to 31st December 2010. Various DEA models are utilised including CCR, BCC and SBM DEA models with various orientations, and also versions of these DEA models which make use of the SORM procedure. These are used to carry out an initial evaluation of the managerial performance of the OEICs/UTs, before two of these DEA models are combined with SFA regression analysis in a three-stage DEA-SFA-DEA methodology to purge the influence of environmental factors and statistical noise, thus leading to a more robust evaluation of the true managerial performance of the OEICs/UTs under assessment. The results of this thesis extend support to the premise of the Efficient Market Hypothesis (EMH) that financial markets are information efficient , and thus it is not possible, given the information available when the investment is made, to consistently obtain returns in excess of the average market return on a risk-adjusted basis, and this thesis does so through the use of a novel approach.

In this thesis we study decentralized dynamics for non-cooperative and cooperative games. The dynamics are behaviorally motivated and assume that very little information is available about other players' preferences, actions, or payoffs. For example, this is the case in markets where exchanges are frequent and the sheer size of the market hinders participants from learning about others' preferences. We consider learning dynamics that are based on trial-and-error and aspiration-based heuristics. Players occasionally try to increase their performance given their current payoffs. If successful they stick to the new action, otherwise they revert to their old action. We also study a dynamic model of social influence based on findings in sociology and psychology that people have a propensity to conform to others' behavior irrespective of the payoff consequences. We analyze the dynamics with a particular focus on two questions: How long does it take to reach equilibrium and what are the stability and welfare properties of the equilibria that the process selects? These questions are at the core of understanding which equilibrium concepts are robust in environments where players have little information about the game and the high rationality assumptions of standard game theory are not very realistic. Methodologically, this thesis builds on game theoretic techniques and prominent solution concepts such as the Nash equilibrium for non-cooperative games and the core for cooperative games, as well as refinement concepts like stochastic stability. The proofs rely on mathematical techniques from random walk theory and integer programming.