Multi-disciplinary ship synthesis models and multi-objective optimization techniques are increasingly being used in ship design. Multi-disciplinary models allow designers to break away from the traditional design spiral approach and focus on searching the design space for the best overall design instead of the best discipline-specific design. Complex design problems such as these often have high levels of uncertainty associated with them, and since most optimization algorithms tend to push solutions to constraint boundaries, the calculated "best" solution might be infeasible if there are minor uncertainties related to the model or problem definition. Consequently, there is a need to address uncertainty in optimization problems to produce effective and reliable results. This thesis focuses on adding a third objective, uncertainty, to the effectiveness and cost objectives already present in a multi-disciplinary ship synthesis model. Uncertainty is quantified using a "confidence of success" (CoS) calculation based on the mean value method. CoS is the probability that a design will satisfy all constraints and meet performance objectives. This work proves that the CoS concept can be applied to synthesis models to estimate uncertainty early in the design process. Multiple sources of uncertainty are realistically quantified and represented in the model in order to investigate their relative importance to the overall uncertainty. This work also presents methods to encourage a uniform distribution of points across the Pareto front. With a well defined front, designs can be selected and refined using a gradient based optimization algorithm to optimize a single objective while holding the others fixed. / Master of Science

Many concrete problems, ranging from Portfolio selection to Water resource management, may be cast into a multiobjective programming framework. The simplistic way of superseding blindly conflictual goals by one objective function let no chance to the model but to churn out meaningless outcomes. Hence interest of discussing ways for tackling Multiobjective Programming Problems. More than this, in many real-life situations, uncertainty and imprecision are in the state of affairs. In this dissertation we discuss ways for solving Multiobjective Programming Problems with fixed and fuzzy coefficients. No preference, a priori, a posteriori, interactive and metaheuristic methods are discussed for the deterministic case. As far as the fuzzy case is concerned, two approaches based respectively on possibility measures and on Embedding Theorem for fuzzy numbers are described. A case study is also carried out for the sake of illustration. We end up with some concluding remarks along with lines for further development, in this field. / Operations Research / M. Sc. (Operations Research)

This study is conducted with the aims to develop a new ranking method for multi-criteria decision making problem with conflicting criteria. Such a problem has a set of Pareto solutions, where the act of improving a value of one solution will result in depreciating some of the others. Thus, in this type of problem, there is no unique solution. However, out of many available options, the Decision Maker eventually has to choose only one solution. With this problem as the motivation, the current study develops a compromise ranking algorithm, namely a trade-off ranking method. The trade-off ranking method able to give a trade-off solution with the least compromise compared to other choices as the best solution. The properties of the algorithm are studied in the thesis on several test cases. The proposed method is compared against several multi-criteria decision making methods with ranking based on the distance measure, which are the TOPSIS, relative distance and VIKOR. The sensitivity analysis and uncertainty test are carried out to examine the methods robustness. A critical criteria analysis is also done to test for the most critical criterion in a multi-criteria problem. The decision making method is considered further in a fuzzy environment problem where the fuzzy trade-off ranking is developed and compared against existing fuzzy decision making methods.

In the era of "big data revolution," marked by an exponential growth of information, extracting value from data enables analysts and businesses to address challenging problems such as drug discovery, fraud detection, and earthquake predictions. Multi-Criteria Decision Support (MCDS) queries are at the core of big-data analytics resulting in several classes of MCDS queries such as OLAP, Top-K, Pareto-optimal, and nearest neighbor queries. The intuitive nature of specifying multi-dimensional preferences has made Pareto-optimal queries, also known as skyline queries, popular. Existing skyline algorithms however do not address several crucial issues such as performing skyline evaluation over disparate sources, progressively generating skyline results, or robustly handling workload with multiple skyline over join queries. In this dissertation we thoroughly investigate topics in the area of skyline-aware query evaluation. In this dissertation, we first propose a novel execution framework called SKIN that treats skyline over joins as first class citizens during query processing. This is in contrast to existing techniques that treat skylines as an "add-on," loosely integrated with query processing by being placed on top of the query plan. SKIN is effective in exploiting the skyline characteristics of the tuples within individual data sources as well as across disparate sources. This enables SKIN to significantly reduce two primary costs, namely the cost of generating the join results and the cost of skyline comparisons to compute the final results. Second, we address the crucial business need to report results early; as soon as they are being generated so that users can formulate competitive decisions in near real-time. On top of SKIN, we built a progressive query evaluation framework ProgXe to transform the execution of queries involving skyline over joins to become non-blocking, i.e., to be progressively generating results early and often. By exploiting SKIN's principle of processing query at multiple levels of abstraction, ProgXe is able to: (1) extract the output dependencies in the output spaces by analyzing both the input and output space, and (2) exploit this knowledge of abstract-level relationships to guarantee correctness of early output. Third, real-world applications handle query workloads with diverse Quality of Service (QoS) requirements also referred to as contracts. Time sensitive queries, such as fraud detection, require results to progressively output with minimal delay, while ad-hoc and reporting queries can tolerate delay. In this dissertation, by building on the principles of ProgXe we propose the Contract-Aware Query Execution (CAQE) framework to support the open problem of contract driven multi-query processing. CAQE employs an adaptive execution strategy to continuously monitor the run-time satisfaction of queries and aggressively take corrective steps whenever the contracts are not being met. Lastly, to elucidate the portability of the core principle of this dissertation, the reasoning and query processing at different levels of data abstraction, we apply them to solve an orthogonal research question to auto-generate recommendation queries that facilitate users in exploring a complex database system. User queries are often too strict or too broad requiring a frustrating trial-and-error refinement process to meet the desired result cardinality while preserving original query semantics. Based on the principles of SKIN, we propose CAPRI to automatically generate refined queries that: (1) attain the desired cardinality and (2) minimize changes to the original query intentions. In our comprehensive experimental study of each part of this dissertation, we demonstrate the superiority of the proposed strategies over state-of-the-art techniques in both efficiency, as well as resource consumption.

This thesis addresses two important classes of optimization : multiobjective optimization and bilevel optimization. The investigation concerns their solution methods, applications, and possible links between them. First of all, we develop a procedure for solving Multiple Objective Linear Programming Problems (MOLPP). The method is based on a new characterization of efficient faces. It exploits the connectedness property of the set of ideal tableaux associated to degenerated points in the case of degeneracy. We also develop an approach for solving Bilevel Linear Programming Problems (BLPP). It is based on the result that an optimal solution of the BLPP is reachable at an extreme point of the underlying region. Consequently, we develop a pivoting technique to find the global optimal solution on an expanded tableau that represents the data of the BLPP. The solutions obtained by our algorithm on some problems available in the literature show that these problems were until now wrongly solved. Some applications of these two areas of optimization problems are explored. An application of multicriteria optimization techniques for finding an optimal planning for the distribution of electrical energy in Cameroon is provided. Similary, a bilevel optimization model that could permit to protect any economic sector where local initiatives are threatened is proposed. Finally, the relationship between the two classes of optimization is investigated. We first look at the conditions that guarantee that the optimal solution of a given BPP is Pareto optimal for both upper and lower level objective functions. We then introduce a new relation that establishes a link between MOLPP and BLPP. Moreover, we show that, to solve a BPP, it is possible to solve two artificial M0PPs. In addition, we explore Bilevel Multiobjective Programming Problem (BMPP), a case of BPP where each decision maker (DM) has more than one objective function. Given a MPP, we show how to construct two artificial M0PPs such that any point that is efficient for both problems is also efficient for the BMPP. For the linear case specially, we introduce an artificial MOLPP such that its resolution can permit to generate the whole feasible set of the leader DM. Based on this result and depending on whether the leader can evaluate or not his preferences for his different objective functions, two approaches for obtaining efficient solutions are presented

Made available in DSpace on 2014-06-11T19:30:51Z (GMT). No. of bitstreams: 0 Previous issue date: 2006-11-23Bitstream added on 2014-06-13T21:01:26Z : No. of bitstreams: 1 ochoapizzali_lf_dr_ilha.pdf: 1694440 bytes, checksum: e159d13557d3d0a89139b7565f849244 (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Alban / Fundação de Ensino Pesquisa e Extensão de Ilha Solteira (FEPISA) / Neste trabalho, é apresentada uma análise em regime permanente que considera a avaliação de impactos técnicos tais como perdas elétricas, queda de tensão e níveis de curto-circuito, entre outros; utilizando dados de demanda e geração variáveis no tempo ao longo de um horizonte determinado. O objetivo é encontrar um conjunto de arranjos da GD (configurações) que levem ao melhor desempenho da rede de distribuição analisada, minimizando ou maximizando cada aspecto técnico segundo o interesse da empresa de distribuição. Dada a natureza combinatória deste problema, que requer uma ferramenta de otimização capaz de manipular múltiplos objetivos, os impactos técnicos serão avaliados simultaneamente utilizando uma metodologia baseada no conceito do Non-dominated Sorting Genetic Algorithm (NSGA), conduzindo a soluções mais reais e diversificadas para a tomada de decisões, conhecidas como soluções ótimas de Pareto. / In this work a steady-state analysis considering the assessment of technical impacts such as losses, voltage drop and short-circuit levels, among others; utilizing time-variant loads and generation within a specified horizon. The objective is to find a set of configurations that lead to the best performance of the distribution network under analysis, minimizing or maximizing each technical aspect according to the utility's concerns. Given the combinatorial nature of this problem, which requires an optimization tool able to handle multiple objectives, technical impacts will be assessed simultaneously through a methodology based on the non-dominated sorting genetic algorithm (NSGA). This approach leads to a more realistic and diversified set of solutions for taking decisions, known as Pareto-optimal solutions.

Many concrete problems, ranging from Portfolio selection to Water resource management, may be cast into a multiobjective programming framework. The simplistic way of superseding blindly conflictual goals by one objective function let no chance to the model but to churn out meaningless outcomes. Hence interest of discussing ways for tackling Multiobjective Programming Problems. More than this, in many real-life situations, uncertainty and imprecision are in the state of affairs. In this dissertation we discuss ways for solving Multiobjective Programming Problems with fixed and fuzzy coefficients. No preference, a priori, a posteriori, interactive and metaheuristic methods are discussed for the deterministic case. As far as the fuzzy case is concerned, two approaches based respectively on possibility measures and on Embedding Theorem for fuzzy numbers are described. A case study is also carried out for the sake of illustration. We end up with some concluding remarks along with lines for further development, in this field. / Operations Research / M. Sc. (Operations Research)

Orientador: Antonio Padilha Feltrin / Banca: Rubén Augusto Romero Lázaro / Banca: Dionízio Paschoareli Júnior / Banca: Gareth Harrison / Banca: Carmen Lucia Tancredo Borges / Resumo: Neste trabalho, é apresentada uma análise em regime permanente que considera a avaliação de impactos técnicos tais como perdas elétricas, queda de tensão e níveis de curto-circuito, entre outros; utilizando dados de demanda e geração variáveis no tempo ao longo de um horizonte determinado. O objetivo é encontrar um conjunto de arranjos da GD (configurações) que levem ao melhor desempenho da rede de distribuição analisada, minimizando ou maximizando cada aspecto técnico segundo o interesse da empresa de distribuição. Dada a natureza combinatória deste problema, que requer uma ferramenta de otimização capaz de manipular múltiplos objetivos, os impactos técnicos serão avaliados simultaneamente utilizando uma metodologia baseada no conceito do Non-dominated Sorting Genetic Algorithm (NSGA), conduzindo a soluções mais reais e diversificadas para a tomada de decisões, conhecidas como soluções ótimas de Pareto. / Abstract: In this work a steady-state analysis considering the assessment of technical impacts such as losses, voltage drop and short-circuit levels, among others; utilizing time-variant loads and generation within a specified horizon. The objective is to find a set of configurations that lead to the best performance of the distribution network under analysis, minimizing or maximizing each technical aspect according to the utility's concerns. Given the combinatorial nature of this problem, which requires an optimization tool able to handle multiple objectives, technical impacts will be assessed simultaneously through a methodology based on the non-dominated sorting genetic algorithm (NSGA). This approach leads to a more realistic and diversified set of solutions for taking decisions, known as Pareto-optimal solutions. / Doutor

How can we predict key decisions made by organizations in the presence of big data and on-demand information? In this dissertation we exploit a large repository of B2B real-time transactional data with service quality indicators and present evidence that organizational decision analytics apply both rational and boundedly-rational (i.e. behavioral) economic models. The dissertation’s findings demonstrate that both utility and heuristic models, respectively, play significant roles in predicting organizational decisions on churn, a key decision in this context. In the presence of a large data set the assumed rationality of organizations appears to provide accurate predictions in uncontrolled experiences and selected boundedly-rational decision rules appear to cause somatic states that make organizations more sensitive to past total qualities of service. This dissertation makes significant new contributions to the understanding of how organizations can effectively use big data to make key operational decisions. As a managerial implication, organizations must be alert to heuristics that might exacerbate the impact of total service pain on customer’s decision to churn.

The facts that relative consumption concerns may give rise both to positional and environmental externalities, and that these two externalities are increasingly transboundary require us to derive an optimal commodity tax in an international framework. The corrective tax policy decided at a national level is found to fail to internalize all positional and environmental externalities. The optimal tax policy under an international cooperative framework reflects correction for both global positional and environmental externalities. In this broader framework, we also characterize the provision of pollution abatement as an additional policy instrument. The results show that relative concerns for one of the private goods do not lead to any modification of the policy rule for public abatement.