Submitted by DOUGLAS MIRANDA RODRIGUES null (douglasnet@gmail.com) on 2016-10-20T13:35:28Z No. of bitstreams: 1 Dissertação - Douglas Miranda Rodrigues.pdf: 8525562 bytes, checksum: de3f28242f3d17929bb0eb8ddc912f55 (MD5) / Approved for entry into archive by Juliano Benedito Ferreira (julianoferreira@reitoria.unesp.br) on 2016-10-26T18:00:28Z (GMT) No. of bitstreams: 1 rodrigues_dm_me_guara.pdf: 8525562 bytes, checksum: de3f28242f3d17929bb0eb8ddc912f55 (MD5) / Made available in DSpace on 2016-10-26T18:00:28Z (GMT). No. of bitstreams: 1 rodrigues_dm_me_guara.pdf: 8525562 bytes, checksum: de3f28242f3d17929bb0eb8ddc912f55 (MD5) Previous issue date: 2016-10-10 / Em diversas áreas de trabalho, da Engenharia à Economia, os problemas se apresentam como sendo multiobjetivos, característica que torna complexa a tomada de decisão. Geralmente, estes objetivos são conflitantes e faz-se necessário o uso de técnicas de otimização para a obtenção de melhores resultados. Na presente dissertação serão estudados alguns métodos para a resolução destes problemas, com o objetivo de aplicar métodos de aglutinação em problemas de projetos de experimentos com múltiplas respostas. Deste modo, inicialmente foi realizada uma análise bibliométrica sobre os diferentes métodos utilizados para a resolução destes problemas. A partir disto, foi desenvolvida uma nova abordagem, utilizando a Programação por Compromisso (Compromise Programming – CP) e a Programação por Metas (Goal Programming – GP), bem como diferentes algoritmos (Gradiente Reduzido Generalizado – GRG e a metaheurística do software Optquest) que são usualmente adotados, com comparação de resultados e análise. De modo geral, esta nova proposta apresentou resultados melhores em relação à abordagem tradicional (desirability), qualificando este procedimento como uma alternativa na otimização de múltiplas respostas. / In many areas, from Engineering to Economics, problems present themselves as multiobjective, which makes a decision-making process complex. Generally, these are conflicting objectives, and optimization techniques are necessary to achieve better results. This paper applies agglutination methods in classical problems of design of experiments with multiple responses. A bibliometric analysis was made, and a new approach was developed, using Compromise Programming – CP and Goal Programming – GP, as well as two different algorithms (Generalized Reduced Gradient – GRG and Optquest’s software metaheuristics) with results comparison and analysis. The new proposal presented better results when compared to the traditional approach (desirability), qualifying this procedure as an alternative in multiple responses optimization.

Orientador: Fernando Augusto da Silva Marins / Resumo: Em diversas áreas de trabalho, da Engenharia à Economia, os problemas se apresentam como sendo multiobjetivos, característica que torna complexa a tomada de decisão. Geralmente, estes objetivos são conflitantes e faz-se necessário o uso de técnicas de otimização para a obtenção de melhores resultados. Na presente dissertação serão estudados alguns métodos para a resolução destes problemas, com o objetivo de aplicar métodos de aglutinação em problemas de projetos de experimentos com múltiplas respostas. Deste modo, inicialmente foi realizada uma análise bibliométrica sobre os diferentes métodos utilizados para a resolução destes problemas. A partir disto, foi desenvolvida uma nova abordagem, utilizando a Programação por Compromisso (Compromise Programming – CP) e a Programação por Metas (Goal Programming – GP), bem como diferentes algoritmos (Gradiente Reduzido Generalizado – GRG e a metaheurística do software Optquest) que são usualmente adotados, com comparação de resultados e análise. De modo geral, esta nova proposta apresentou resultados melhores em relação à abordagem tradicional (desirability), qualificando este procedimento como uma alternativa na otimização de múltiplas respostas. / Mestre

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)

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)

Orientadores: Antonio Carlos Moretti, Roberto Andreani / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica / Made available in DSpace on 2018-08-13T15:55:52Z (GMT). No. of bitstreams: 1 Silva_DanielTressida_M.pdf: 563016 bytes, checksum: 89e68063d06bd89084d7d6a15fdb7403 (MD5) Previous issue date: 2009 / Resumo: Nesta dissertação, estudamos algoritmos genéticos para resolver o problema de corte unidimensional multiobjetivo, onde minimizamos o desperdício dos objetos processados e o número de padrões distintos denominado custo de setup. Primeiro, realizamos uma codificação baseada em grupos desenvolvida por Falkenauer e, em seguida, aplicamos o algoritmo genético multiobjetivo SPEA2 para obter a Fronteira de Eficiente do problema. / Abstract: In this dissertation we studied genetic algorithms to solve the unidimensional multiobjective cutting stock problem, where we minimize the wastage of processed objects and the distinct number of patterns used, called setup cost. First, we make a group based codification derived by Falkenauer and, after that, we apply the multiobjective genetic algorithm SPEA2 to obtain problem's Efficient Frontier. / Mestrado / Otimização e Pesquisa Operacional / Mestre em Matemática Aplicada

Orientadores: Akebo Yamakami, Jose Luis Verdegay Galdeano / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-14T06:44:19Z (GMT). No. of bitstreams: 1 Silva_RicardoCoelho_D.pdf: 1144878 bytes, checksum: 38379443fb6892fd6eda74c55c3b99dc (MD5) Previous issue date: 2009 / Resumo: O objetivo deste trabalho é buscar, estudar e estabelecer as condições de otimali-dade para resolver problemas de programação multi-objetivo irrestritos e restritos em um ambiente impreciso. Essas imprecisões estão presentes nos problemas da vida real e existem muitas formas de tratá-las, mas nesse trabalho será usado a teoria de conjuntos nebulosos. Utilizando como base a otimização nebulosa, foram desenvolvidas duas abordagens para resolver problemas multi-objetivo nebulosos. A primeira abordagem transforma um problema nebuloso em um problema clássico paramétrico com um número maior de funções objetivo, a qual é chamada de paramétrica. A segunda abordagem, chamada de possibilística, usa a teoria de possibilidade como um índice de comparação entre números nebulosos com a finalidade de garantir condições de otimalidade em um ambiente nebuloso. Alguns exemplos numéricos são resolvidos usando um algoritmo genético chamado NSGA-II elitista, com algumas modificações para a comparação de números nebulosos, e depois feita uma análise dos resultados encontrados por ambos os enfoques. / Abstract: The main goal of this work is to search, study and present the optimality conditions to solve the unconstraint and constraint multiobjetive programming problems in imprecise environment. These imprécisions can be found in the real-world optimization problems and there are utmost ways for dealing with them, but in this work will be used the theory of fuzzy sets. Using as a basis the fuzzy optimization, two approaches were developed to solve fuzzy multiobjective problems. The first approach transforms a fuzzy problem into a parametric classic multiobjective programming problem with many more objective functions, which is called parametric approach. The second one, called possibilistic, uses the possibility theory as a comparison index between two fuzzy numbers in order to ensure optimality conditions in a fuzzy environment. Some numerical examples are solved by using a genetic algorithm called elitist NSGA-II with some modifications to compare fuzzy numbers, and then the results obtained with both approaches are analysed. / Doutorado / Automação / Doutor em Engenharia Elétrica

No / A steel hot rolling mill subjects steel slabs to high temperatures and pressures in order to form steel coils. We describe the scheduling problem for a steel hot rolling mill. We detail the operation of a commercial decision support system which provides semi-automatic schedules, comparing its operation with existing, manual planning procedures. This commercial system is currently in use in several steel mills worldwide. The system features a very detailed multiobjective model of the steel hot rolling process. This model is solved using a variety of bespoke local and Tabu search heuristics. We describe both this model and the heuristics used to solve it. The production environment is highly unstable with frequent, unforeseen events interrupting planned production. We describe how the scheduling system's models, algorithms and interfaces have been developed to handle this instability. We consider particularly the impact on existing planning and production systems and the qualitative improvements which result from the system's implementation.

Made available in DSpace on 2014-12-17T15:47:55Z (GMT). No. of bitstreams: 1 SilviaMDM_DISSERT.pdf: 1535044 bytes, checksum: 925f2f885f42335d55c35aa64bb4d026 (MD5) Previous issue date: 2011-02-17 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / The Multiobjective Spanning Tree is a NP-hard Combinatorial Optimization problem whose application arises in several areas, especially networks design. In this work, we propose a solution to the biobjective version of the problem through a Transgenetic Algorithm named ATIS-NP. The Computational Transgenetic is a metaheuristic technique from Evolutionary Computation whose inspiration relies in the conception of cooperation (and not competition) as the factor of main influence to evolution. The algorithm outlined is the evolution of a work that has already yielded two other transgenetic algorithms. In this sense, the algorithms previously developed are also presented. This research also comprises an experimental analysis with the aim of obtaining information related to the performance of ATIS-NP when compared to other approaches. Thus, ATIS-NP is compared to the algorithms previously implemented and to other transgenetic already presented for the problem under consideration. The computational experiments also address the comparison to two recent approaches from literature that present good results, a GRASP and a genetic algorithms. The efficiency of the method described is evaluated with basis in metrics of solution quality and computational time spent. Considering the problem is within the context of Multiobjective Optimization, quality indicators are adopted to infer the criteria of solution quality. Statistical tests evaluate the significance of results obtained from computational experiments / A ?rvore Geradora Multiobjetivo ? um problema de Otimiza??o Combinat?ria NP-?rduo. Esse problema possui aplica??o em diversas ?reas, em especial, no projeto de redes. Nesse trabalho, prop?e-se uma solu??o para o problema em sua vers?o biobjetivo por meio de um Algoritmo Transgen?tico, denominado ATIS-NP. A Transgen?tica Computacional ? uma t?cnica metaheur?stica da Computa??o Evolucion?ria cuja inspira??o est? na coopera??o (e n?o na competi??o) como fator de maior influ?ncia para a evolu??o. O algoritmo proposto ? a evolu??o de um trabalho que j? originou dois outros algoritmos transgen?ticos. Nesse sentido, os algoritmos previamente desenvolvidos tamb?m s?o apresentados. Essa pesquisa compreende ainda uma an?lise experimental que visa obter informa??es quanto ao desempenho do ATIS-NP quando comparado a outros algoritmos. Para tanto, o ATIS-NP ? comparado aos dois algoritmos anteriormente implementados, bem como a outro transgen?tico proposto na literatura para o problema tratado. Os experimentos computacionais abrangem ainda a compara??o do algoritmo desenvolvido a duas abordagens recentes da literatura que obt?m excelentes resultados, um GRASP e um gen?tico. A efici?ncia do m?todo apresentado ? avaliada com base em medidas de qualidade de solu??o e tempo computacional despendido. Uma vez que o problema se insere no contexto da Otimiza??o Multiobjetivo, indicadores de qualidade s?o utilizados para inferir o crit?rio de qualidade de solu??es obtidas. Testes estat?sticos avaliam a signific?ncia dos resultados obtidos nos experimentos computacionais

Multiobjective Stochastic Linear Programming is a relevant topic. As a matter of fact, many real life problems ranging from portfolio selection to water resource management may be cast into this framework. There are severe limitations in objectivity in this field due to the simultaneous presence of randomness and conflicting goals. In such a turbulent environment, the mainstay of rational choice does not hold and it is virtually impossible to provide a truly scientific foundation for an optimal decision. In this thesis, we resort to the bounded rationality and chance-constrained principles to define satisficing solutions for Multiobjective Stochastic Linear Programming problems. These solutions are then characterized for the cases of normal, exponential, chi-squared and gamma distributions. Ways for singling out such solutions are discussed and numerical examples provided for the sake of illustration. Extension to the case of fuzzy random coefficients is also carried out. / Decision Sciences

Multiobjective Stochastic Linear Programming is a relevant topic. As a matter of fact, many real life problems ranging from portfolio selection to water resource management may be cast into this framework. There are severe limitations in objectivity in this field due to the simultaneous presence of randomness and conflicting goals. In such a turbulent environment, the mainstay of rational choice does not hold and it is virtually impossible to provide a truly scientific foundation for an optimal decision. In this thesis, we resort to the bounded rationality and chance-constrained principles to define satisficing solutions for Multiobjective Stochastic Linear Programming problems. These solutions are then characterized for the cases of normal, exponential, chi-squared and gamma distributions. Ways for singling out such solutions are discussed and numerical examples provided for the sake of illustration. Extension to the case of fuzzy random coefficients is also carried out. / Decision Sciences