Made available in DSpace on 2016-03-22T17:26:43Z (GMT). No. of bitstreams: 1 marilia.pdf: 1002551 bytes, checksum: 1472f2853b044a1ac45b6b20cb05f4be (MD5) Previous issue date: 2011-04-20 / In fuzzy logic, the interval valued fuzzy propositions can be combined using different aggregations (interval t-norms, interval t-conorms) and interval negations, generating new interval implications. The interval extension of fuzzy sets plays a crucial role in providing the foundation for the development of inference rules in expert systems based on interval valued fuzzy logic. Most fuzzy implication operators and their corresponding interval extensions are based on two types of representations: (i) the explicit representations defined in terms of aggregation operators,such as the classes of S-implications, QL-implications and D-implications; and (ii) implicit representations, considering forinstance R-implications. However, some fuzzy implication operations often applied in expert systems can not be classified in one of these two representations. In this new class of implications, referred to as A-implications, the relations with the aggregation operators are axiomatically defined based on algebraic properties. Therefore, to describe an interval extension of these operators, this study focuses on Yager s implications, Gh funtions and related properties of interval valued fuzzy implications, which can not be naturally represented explicitly or implicitly. Based on such study, this work introduces the canonical interval representation of the Yager s implications and Gh implication. In addition, it includes an analysis of the action of interval automorphisms on the class of interval valued A-implications and related algebraic properties which are verified by this interval constructions / Na l´ogica fuzzy, as proposic¸ oes fuzzy valoradas intervalarmente podem ser combinadas utilizando-se diferentes operadores de agregac¸ ao (t-normas intervalares, t-conormas intervalares) e o complemento intervalar, gerando novos operadores de implicac¸ oes intervalares. Na extens ao intervalar dos conjuntos fuzzy, as implicac¸ oes fuzzy intervalares t em um papel fundamental fornecendo a fundamentac¸ ao para o desenvolvimento das regras de infer encias em sistemas especialistas baseados na l´ogica fuzzy intervalar. Para a an´alise de propriedades alg´ebricas, a maioria dos operadores de implicac¸ oes fuzzy e suas correspondentes extens oes intervalares, est ao baseados em duas formas de representac¸ ao: (i) expl´ıcita, definida em termos dos operadores de agregac¸ ao, como verificam-se nas classes de S-implicac¸ oes, QL-implicac¸ oes e D-implicac¸ oes; ou, ainda (ii) impl´ıcita, como as Rimplicac ¸ oes. No entanto, algumas operac¸ oes de implicac¸ ao fuzzy frequentemente aplicadas em sistemas especialistas n ao se enquadram em uma destas formas de representac¸ ao. Esta nova classe de implicac¸ ao ´e referenciada como A-implicac¸ oes, onde as relac¸ oes com os operadores de agregac¸ ao s ao definidas a partir de uma axiomatizac¸ ao baseada em propriedades alg´ebricas. Portanto, para descrever a extens ao intervalar destes operadores, neste trabalho estuda-se a axiomatizac¸ ao das implicac¸ oes de Yager e da Gh-implicac¸ ao. Com base em tal estudo, este trabalho introduz a representac¸ ao can onica intervalar das implicac¸ oes de Yager e Gh-implicac¸ ao. Al´em disso, inclui uma an´alise da ac¸ ao de automorfismos intervalares sobre estas classes de A-implicac¸ oes valoradas intervalarmente relacionando as propriedades alg´ebricas que s ao verificadas por estas construc¸ oes intervalares

A Fuzzy Preference Framework for the Graph Model for Conflict Resolution (FGM) is developed so that real-world conflicts in which decision makers (DMs) have uncertain preferences can be modeled and analyzed mathematically in order to gain strategic insights. The graph model methodology constitutes both a formal representation of a multiple participant-multiple objective decision problem and a set of analysis procedures that provide insights into them. Because crisp or definite preference is a special case of fuzzy preference, the new framework of the graph model can include---and integrate into the analysis---both certain and uncertain information about DMs' preferences. In this sense, the FGM is an important generalization of the existing graph model for conflict resolution. One key contribution of this study is to extend the four basic graph model stability definitions to models with fuzzy preferences. Together, fuzzy Nash stability, fuzzy general metarationality, fuzzy symmetric metarationality, and fuzzy sequential stability provide a realistic description of human behavior under conflict in the face of uncertainty. A state is fuzzy stable for a DM if a move to any other state is not sufficiently likely to yield an outcome the DM prefers, where sufficiency is measured according to a fuzzy satisficing threshold that is characteristic of the DM. A fuzzy equilibrium, an outcome that is fuzzy stable for all DMs, therefore represents a possible resolution of the conflict. To demonstrate their applicability, the fuzzy stability definitions are applied to a generic two-DM sustainable development conflict, in which a developer plans to build or operate a project inspected by an environmental agency. This application identifies stable outcomes, and thus clarifies the necessary conditions for sustainability. The methodology is then applied to an actual dispute with more than two DMs concerning groundwater contamination that took place in Elmira, Ontario, Canada, again uncovering valuable strategic insights. To investigate how DMs with fuzzy preferences can cooperate in a strategic conflict, coalition fuzzy stability concepts are developed within FGM. In particular, coalition fuzzy Nash stability, coalition fuzzy general metarationality, coalition fuzzy symmetric metarationality, and coalition fuzzy sequential stability are defined, for both a coalition and a single DM. These concepts constitute a natural generalization of the corresponding non-cooperative fuzzy preference-based definitions for Nash stability, general metarationality, symmetric metarationality, and sequential stability, respectively. As a follow-up analysis of the non-cooperative fuzzy stability results and to demonstrate their applicability, the coalition fuzzy stability definitions are applied to the aforementioned Elmira groundwater contamination conflict. These new concepts can be conveniently utilized in the study of practical problems in order to gain strategic insights and to compare conclusions derived from both cooperative and non-cooperative stability notions. A fuzzy option prioritization technique is developed within the FGM so that uncertain preferences of DMs in strategic conflicts can be efficiently modeled as fuzzy preferences by using the fuzzy truth values they assign to preference statements about feasible states. The preference statements of a DM express desirable combinations of options or courses of action, and are listed in order of importance. A fuzzy truth value is a truth degree, expressed as a number between 0 and 1, capturing uncertainty in the truth of a preference statement at a feasible state. It is established that the output of a fuzzy preference formula, developed based on the fuzzy truth values of preference statements, is always a fuzzy preference relation. The fuzzy option prioritization methodology can also be employed when the truth values of preference statements at feasible states are formally based on Boolean logic, thereby generating a crisp preference over feasible states that is the same as would be found using the existing crisp option prioritization approach. Therefore, crisp option prioritization is a special case of fuzzy option prioritization. To demonstrate how this methodology can be used to represent fuzzy preferences in real-world problems, the new fuzzy option prioritization technique is applied to the Elmira aquifer contamination conflict. It is observed that the fuzzy preferences obtained by employing this technique are very close to those found using the rather complicated and tedious pairwise comparison approach.

A Fuzzy Preference Framework for the Graph Model for Conflict Resolution (FGM) is developed so that real-world conflicts in which decision makers (DMs) have uncertain preferences can be modeled and analyzed mathematically in order to gain strategic insights. The graph model methodology constitutes both a formal representation of a multiple participant-multiple objective decision problem and a set of analysis procedures that provide insights into them. Because crisp or definite preference is a special case of fuzzy preference, the new framework of the graph model can include---and integrate into the analysis---both certain and uncertain information about DMs' preferences. In this sense, the FGM is an important generalization of the existing graph model for conflict resolution. One key contribution of this study is to extend the four basic graph model stability definitions to models with fuzzy preferences. Together, fuzzy Nash stability, fuzzy general metarationality, fuzzy symmetric metarationality, and fuzzy sequential stability provide a realistic description of human behavior under conflict in the face of uncertainty. A state is fuzzy stable for a DM if a move to any other state is not sufficiently likely to yield an outcome the DM prefers, where sufficiency is measured according to a fuzzy satisficing threshold that is characteristic of the DM. A fuzzy equilibrium, an outcome that is fuzzy stable for all DMs, therefore represents a possible resolution of the conflict. To demonstrate their applicability, the fuzzy stability definitions are applied to a generic two-DM sustainable development conflict, in which a developer plans to build or operate a project inspected by an environmental agency. This application identifies stable outcomes, and thus clarifies the necessary conditions for sustainability. The methodology is then applied to an actual dispute with more than two DMs concerning groundwater contamination that took place in Elmira, Ontario, Canada, again uncovering valuable strategic insights. To investigate how DMs with fuzzy preferences can cooperate in a strategic conflict, coalition fuzzy stability concepts are developed within FGM. In particular, coalition fuzzy Nash stability, coalition fuzzy general metarationality, coalition fuzzy symmetric metarationality, and coalition fuzzy sequential stability are defined, for both a coalition and a single DM. These concepts constitute a natural generalization of the corresponding non-cooperative fuzzy preference-based definitions for Nash stability, general metarationality, symmetric metarationality, and sequential stability, respectively. As a follow-up analysis of the non-cooperative fuzzy stability results and to demonstrate their applicability, the coalition fuzzy stability definitions are applied to the aforementioned Elmira groundwater contamination conflict. These new concepts can be conveniently utilized in the study of practical problems in order to gain strategic insights and to compare conclusions derived from both cooperative and non-cooperative stability notions. A fuzzy option prioritization technique is developed within the FGM so that uncertain preferences of DMs in strategic conflicts can be efficiently modeled as fuzzy preferences by using the fuzzy truth values they assign to preference statements about feasible states. The preference statements of a DM express desirable combinations of options or courses of action, and are listed in order of importance. A fuzzy truth value is a truth degree, expressed as a number between 0 and 1, capturing uncertainty in the truth of a preference statement at a feasible state. It is established that the output of a fuzzy preference formula, developed based on the fuzzy truth values of preference statements, is always a fuzzy preference relation. The fuzzy option prioritization methodology can also be employed when the truth values of preference statements at feasible states are formally based on Boolean logic, thereby generating a crisp preference over feasible states that is the same as would be found using the existing crisp option prioritization approach. Therefore, crisp option prioritization is a special case of fuzzy option prioritization. To demonstrate how this methodology can be used to represent fuzzy preferences in real-world problems, the new fuzzy option prioritization technique is applied to the Elmira aquifer contamination conflict. It is observed that the fuzzy preferences obtained by employing this technique are very close to those found using the rather complicated and tedious pairwise comparison approach.

Master's thesis deals with the evaluation of suppliers of selected company using fuzzy logic. Designed fuzzy system allows firm to evaluate individual offers and serves as a support for decision-making.

This diploma thesis deals with development of a fuzzy decision-making system for evaluating of REONTECH CZ s.r.o. company suppliers. The first part is dedicated to theory of fuzzy logic. The next part contains firstly an analysis of the current situation and then a description of the development process of the fuzzy decision-making system. The main outcome of this thesis is a tool that meets needs and requirements of the company and serves as a decision-making support tool.

Made available in DSpace on 2014-12-17T14:55:08Z (GMT). No. of bitstreams: 1 ClaudileneGC_TESE.pdf: 853804 bytes, checksum: 011bfb4befb8b54fce2cd4b1b724efdb (MD5) Previous issue date: 2012-08-20 / The idea of considering imprecision in probabilities is old, beginning with the Booles George work, who in 1854 wanted to reconcile the classical logic, which allows the modeling of complete ignorance, with probabilities. In 1921, John Maynard Keynes in his book made explicit use of intervals to represent the imprecision in probabilities. But only from the work ofWalley in 1991 that were established principles that should be respected by a probability theory that deals with inaccuracies. With the emergence of the theory of fuzzy sets by Lotfi Zadeh in 1965, there is another way of dealing with uncertainty and imprecision of concepts. Quickly, they began to propose several ways to consider the ideas of Zadeh in probabilities, to deal with inaccuracies, either in the events associated with the probabilities or in the values of probabilities. In particular, James Buckley, from 2003 begins to develop a probability theory in which the fuzzy values of the probabilities are fuzzy numbers. This fuzzy probability, follows analogous principles to Walley imprecise probabilities. On the other hand, the uses of real numbers between 0 and 1 as truth degrees, as originally proposed by Zadeh, has the drawback to use very precise values for dealing with uncertainties (as one can distinguish a fairly element satisfies a property with a 0.423 level of something that meets with grade 0.424?). This motivated the development of several extensions of fuzzy set theory which includes some kind of inaccuracy. This work consider the Krassimir Atanassov extension proposed in 1983, which add an extra degree of uncertainty to model the moment of hesitation to assign the membership degree, and therefore a value indicate the degree to which the object belongs to the set while the other, the degree to which it not belongs to the set. In the Zadeh fuzzy set theory, this non membership degree is, by default, the complement of the membership degree. Thus, in this approach the non-membership degree is somehow independent of the membership degree, and this difference between the non-membership degree and the complement of the membership degree reveals the hesitation at the moment to assign a membership degree. This new extension today is called of Atanassov s intuitionistic fuzzy sets theory. It is worth noting that the term intuitionistic here has no relation to the term intuitionistic as known in the context of intuitionistic logic. In this work, will be developed two proposals for interval probability: the restricted interval probability and the unrestricted interval probability, are also introduced two notions of fuzzy probability: the constrained fuzzy probability and the unconstrained fuzzy probability and will eventually be introduced two notions of intuitionistic fuzzy probability: the restricted intuitionistic fuzzy probability and the unrestricted intuitionistic fuzzy probability / A id?ia de considerar imprecis?o em probabilidades ? antiga, remontando aos trabalhos de George Booles, que em 1854 pretendia conciliar a l?gica cl?ssica, que permite modelar ignor?ncia completa, com probabilidades. Em 1921, John Maynard Keynes em seu livro fez uso expl?cito de intervalos para representar a imprecis?o nas probabilidades. Por?m, apenas a partir dos trabalhos de Walley em 1991 que foram estabelecidos princ?pios que deveriam ser respeitados por uma teoria de probabilidades que lide com imprecis?es. Com o surgimento da teoria dos conjuntos fuzzy em 1965 por Lotfi Zadeh, surge uma outra forma de lidar com incertezas e imprecis?es de conceitos. Rapidamente, come?aram a se propor diversas formas de considerar as id?ias de Zadeh em probabilidades, para lidar com imprecis?es, seja nos eventos associados ?s probabilidades como aos valores das probabilidades. Em particular, James Buckley, a partir de 2003 come?a a desenvolver uma teoria de probabilidade fuzzy em que os valores das probabilidades sejam n?meros fuzzy. Esta probabilidade fuzzy segue princ?pios an?logos ao das probabilidades imprecisas de Walley. Por outro lado, usar como graus de verdade n?meros reais entre 0 e 1, como proposto originalmente por Zadeh, tem o inconveniente de usar valores muito precisos para lidar com incertezas (como algu?m pode diferenciar de forma justa que um elemento satisfaz uma propriedade com um grau 0.423 de algo que satisfaz com grau 0.424?). Isto motivou o surgimento de diversas extens?es da teoria dos conjuntos fuzzy pelo fato de incorporar algum tipo de imprecis?o. Neste trabalho ? considerada a extens?o proposta por Krassimir Atanassov em 1983, que adicionou um grau extra de incerteza para modelar a hesita??o ao momento de se atribuir o grau de pertin?ncia, e portanto, um valor indicaria o grau com o qual o objeto pertence ao conjunto, enquanto o outro, o grau com o qual n?o pertence. Na teoria dos conjuntos fuzzy de Zadeh, esse grau de n?o-pertin?ncia por defeito ? o complemento do grau de pertin?ncia. Assim, nessa abordagem o grau de n?o-pertin?ncia ? de alguma forma independente do grau de pertin?ncia, e nessa diferencia entre essa n?o-pertin?ncia e o complemento do grau de pertin?ncia revela a hesita??o presente ao momento de se atribuir o grau de pertin?ncia. Esta nova extens?o hoje em dia ? chamada de teoria dos conjuntos fuzzy intuicionistas de Atanassov. Vale salientar, que o termo intuicionista aqui n?o tem rela??o com o termo intuicionista como conhecido no contexto de l?gica intuicionista. Neste trabalho ser? desenvolvida duas propostas de probabilidade intervalar: a probabilidade intervalar restrita e a probabilidade intervalar irrestrita; tamb?m ser?o introduzidas duas no??es de probabilidade fuzzy: a probabilidade fuzzy restrita e a probabilidade fuzzy irrestrita e por fim ser?o introduzidas duas no??es de probabilidade fuzzy intuicionista: a probabilidade fuzzy intuicionista restrita e a probabilidade fuzzy intuicionista irrestrita

A seleção de fornecedores tem impacto significante no custo e na qualidade de produtos manufaturados. Por isso, a seleção de fornecedores passou a ser vista como uma atividade bastante crítica para o desempenho da empresa compradora. Muitos estudos da literatura propõem o uso dos métodos multicritério fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) e fuzzy AHP (Analytic Hierarchy Process) para apoiar a seleção de fornecedores. Contudo, não são encontrados estudos que avaliem o desempenho destes métodos quando usados neste domínio de problema. Diante desta lacuna, este estudo compara os métodos fuzzy TOPSIS (CHEN, 2000) e fuzzy AHP (CHANG, 1996) no apoio à seleção de fornecedores. Esta pesquisa utiliza uma abordagem quantitativa descritiva empírica, baseada em modelagem e simulação. Os métodos fuzzy TOPSIS e fuzzy AHP foram aplicados em um caso ilustrativo de seleção de fornecedores. O desempenho dos fornecedores e o peso dos critérios foram avaliados por um especialista de uma empresa. Modelos de simulação foram implementados usando MATLAB® e aplicados na seleção de fornecedores de uma empresa de uma cadeia de suprimentos automotiva. Cinco fornecedores foram avaliados em relação à qualidade, custo, entrega, perfil e relacionamento. O peso dos critérios e o desempenho dos fornecedores foi avaliado por meio da opinião de um especialista da empresa. Posteriormente, os métodos fuzzy TOPSIS e fuzzy AHP foram comparados em relação à capacidade de apoiar a decisão em grupo, qualificação de fornecedores, escolha final de fornecedores, situações de compra e modelagem de decisões sob incerteza. A eficiência dos métodos em relação à complexidade computacional e à interação requerida com o usuário também foi comparada. Os resultados mostraram que o fuzzy TOPSIS é mais flexível e mais adequado que o fuzzy AHP para modelar diferentes tipos de cenários de seleção de fornecedores. A realização desta discussão é sugerida por Ertugrul e Karakasoglu (2008), e é relevante para ajudar pesquisadores e gestores na escolha de abordagens efetivas para lidar com diferentes cenários de seleção de fornecedores. / Supplier selection has a significant influence on the cost, quality and delivery of products of the buying company. Therefore, supplier selection has become a very critical activity to the performance of the buying company. Several studies presented in the literature propose the use of fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and fuzzy AHP (Analytic Hierarchy Process) to aid the decision process of supplier selection. However, there are no comparative studies of these two methods when applied to the problem of supplier selection. Thus, this paper presents a comparative analysis of the methods fuzzy TOPSIS (Chen, 2000) and fuzzy AHP (Chang, 1996) applied to the problem of supplier selection. A descriptive quantitative approach was adopted as the research method. Algorithms of the methods fuzzy TOPSIS and fuzzy AHP were developed in Matlab© and applied to the selection of suppliers of a company in the automotive production chain. Five suppliers were evaluated regarding quality of conformance, cost, delivery, profile and relationship. The weight of the criteria and the performance of the suppliers were evaluated by specialist opinion from the studied company. The methods Fuzzy TOPSIS e Fuzzy AHP were compared in terms of ability to support the group decision, supplier qualification, final choice of suppliers, buying situations and modeling decisions under uncertainty. The efficiency of the methods with respect to computational complexity and the required user interaction was also compared. The comparative analysis shows that Fuzzy TOPSIS presents better than Fuzzy AHP performance, especially in scenarios in wich many alternatives are evaluated. Thus, Fuzzy TOPSIS is more flexible and appropriate than Fuzzy AHP to deal with supplier selection problem. This paper presents a new study, comparing the methods Fuzzy TOPSIS and Fuzzy AHP. As commented by Ertugrul and Karakasoglu (2008), a study such as this can contribute to the advance of knowledge, helping researchers and practitioners choosing more effective approaches to supplier selection.

Este trabalho apresenta duas metodologias baseadas em lógica fuzzy para automatizar o diagnóstico de defeito em equipamentos mecânicos, além de fazer uma comparação de seu desempenho utilizando um caso experimental. As duas metodologias estudadas são: o sistema de inferência fuzzy e o algoritmo baseado em Fuzzy C-Means. O alarme estatístico é uma metodologia existente atualmente na indústria com este objetivo e que será utilizado neste trabalho para comparação de desempenho. Para realizar os testes, foram desenvolvidos programas que permitiram criar alarmes e sistemas fuzzy utilizando um banco de dados experimental. De modo diferente ao que são feitos normalmente, os sistemas fuzzy de diagnóstico testados neste trabalho foram construídos automaticamente utilizando informações do banco de dados experimentais composto por sinais de vibração, que representam a condição normal e diversos tipos de defeitos em mancais de rolamentos. Os parâmetros escalares característicos necessários para a entrada nos sistemas fuzzy foram obtidos através do processamento dos sinais de vibração de mancais de rolamentos. Nas análises realizadas neste trabalho, foi estudada a influência de diversos características de criação do sistema fuzzy. Como exemplo, pode-se citar como principal influência, a complexidade do banco de dados a ser analisado pelo sistema fuzzy. Por fim, além de apresentar uma comparação de performance entre as metodologias fuzzy apresentadas no trabalho, com o alarme estatístico, são discutidas as características de cada uma destas metodologias. Destacam-se como principais contribuições deste trabalho, a obtenção de uma metodologia utilizada para criar de maneira automática o sistema de inferência fuzzy e as modificações realizadas no algoritmo Fuzzy C-Means para aperfeiçoar o desempenho em classificação de defeitos. / This works describes two proposed methodologies for the automatic diagnoses in mechanical equipment: the fuzzy system inference and a Fuzzy C-Means based algorithm. Their performances are evaluated in an experimental case and, afterwards, also compared by the statistical alarm, a diagnostic methodology very used in industries at present. In order to do the tests, a developed computer algorithm allowed creating alarms and fuzzy systems by the use of an experimental database. These tested diagnostic systems were automatically built using information from the mentioned database that was composed by samples of vibration signals, representing several types of rolling bearing defects and the bearing normal condition. The fuzzy systems input scalar parameters were obtained by signal processing. The influence of some of the building fuzzy systems parameters in the system performance was also studied, which allow establishing, for example, that the database complexity is an important factor in the fuzzy system performance. Finally, this work discusses the main characteristics of each one of the described methodologies. The most important contribution of this work is the proposition of a methodology for creating fuzzy system automatically as well as the analysis of the fuzzy C-Means as a tool for system diagnoses.

Submitted by Aline Batista (alinehb.ufpel@gmail.com) on 2017-05-05T17:33:32Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Diferença Fuzzi intuicionista - robustez, dualidade e conjugação.pdf: 1119948 bytes, checksum: b732f8ac3cf0e1c348e50484c7cc1a05 (MD5) / Approved for entry into archive by Aline Batista (alinehb.ufpel@gmail.com) on 2017-05-05T22:17:27Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Diferença Fuzzi intuicionista - robustez, dualidade e conjugação.pdf: 1119948 bytes, checksum: b732f8ac3cf0e1c348e50484c7cc1a05 (MD5) / Made available in DSpace on 2017-05-05T22:17:38Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Diferença Fuzzi intuicionista - robustez, dualidade e conjugação.pdf: 1119948 bytes, checksum: b732f8ac3cf0e1c348e50484c7cc1a05 (MD5) Previous issue date: 2016-08-22 / Sem bolsa / Esta dissertação foca, sobretudo, nos conceitos fundamentais relativos ao estudo da robustez, dualidade e conjugação na Lógica Fuzzy (FL) e sua extensão intuicionista proposta por Atanassov (A-IFL). A metodologia de avaliação da sensibilidade ponto-a-ponto é aplicada a conectivos fuzzy e conectivos fuzzy intuicionistas, considerando a ação de negações fortes e automorfismos. O objetivo principal neste trabalho consiste na avaliação da robustez de operadores de diferença, representáveis por composição de negações e agregações da LF e da A-IFL. O operador de diferença tem aplicação direta em conceitos da FL e da A-IFL, quando do uso de conceitos de distância, medidas de similaridade e entropia. O trabalho colabora com a investigação da robustez na construção dual da classe de operadores de diferença em LF e A-IFL, incluindo possíveis construções conjugadas obtidas por automorfismos representáveis. / This dissertation focuses mainly on fundamental concepts relating to the study of robustness, duality and in conjunction Fuzzy Logic (FL) and its intuitionistic extension proposed by Atanassov (A-IFL). The methodology for assessing the sensitivity point-to-point is applied to fuzzy connectives and fuzzy connective intuitionists considering the action of strong denials and automorphisms. The main objective of this study is to assess the robustness of di?erence operators, representable by composition of denials and aggregations of LF and A-IFL. The di?erence operator has direct application of the concepts and the FL-IFL, when using distance concepts of similarity and entropy measures. The research work cooperates with the robustness of the dual construction di?erence operator class LF and A-IFL, including possible constructions conjugate obtained by automorphisms representable.

A seleção de fornecedores tem impacto significante no custo e na qualidade de produtos manufaturados. Por isso, a seleção de fornecedores passou a ser vista como uma atividade bastante crítica para o desempenho da empresa compradora. Muitos estudos da literatura propõem o uso dos métodos multicritério fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) e fuzzy AHP (Analytic Hierarchy Process) para apoiar a seleção de fornecedores. Contudo, não são encontrados estudos que avaliem o desempenho destes métodos quando usados neste domínio de problema. Diante desta lacuna, este estudo compara os métodos fuzzy TOPSIS (CHEN, 2000) e fuzzy AHP (CHANG, 1996) no apoio à seleção de fornecedores. Esta pesquisa utiliza uma abordagem quantitativa descritiva empírica, baseada em modelagem e simulação. Os métodos fuzzy TOPSIS e fuzzy AHP foram aplicados em um caso ilustrativo de seleção de fornecedores. O desempenho dos fornecedores e o peso dos critérios foram avaliados por um especialista de uma empresa. Modelos de simulação foram implementados usando MATLAB® e aplicados na seleção de fornecedores de uma empresa de uma cadeia de suprimentos automotiva. Cinco fornecedores foram avaliados em relação à qualidade, custo, entrega, perfil e relacionamento. O peso dos critérios e o desempenho dos fornecedores foi avaliado por meio da opinião de um especialista da empresa. Posteriormente, os métodos fuzzy TOPSIS e fuzzy AHP foram comparados em relação à capacidade de apoiar a decisão em grupo, qualificação de fornecedores, escolha final de fornecedores, situações de compra e modelagem de decisões sob incerteza. A eficiência dos métodos em relação à complexidade computacional e à interação requerida com o usuário também foi comparada. Os resultados mostraram que o fuzzy TOPSIS é mais flexível e mais adequado que o fuzzy AHP para modelar diferentes tipos de cenários de seleção de fornecedores. A realização desta discussão é sugerida por Ertugrul e Karakasoglu (2008), e é relevante para ajudar pesquisadores e gestores na escolha de abordagens efetivas para lidar com diferentes cenários de seleção de fornecedores. / Supplier selection has a significant influence on the cost, quality and delivery of products of the buying company. Therefore, supplier selection has become a very critical activity to the performance of the buying company. Several studies presented in the literature propose the use of fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and fuzzy AHP (Analytic Hierarchy Process) to aid the decision process of supplier selection. However, there are no comparative studies of these two methods when applied to the problem of supplier selection. Thus, this paper presents a comparative analysis of the methods fuzzy TOPSIS (Chen, 2000) and fuzzy AHP (Chang, 1996) applied to the problem of supplier selection. A descriptive quantitative approach was adopted as the research method. Algorithms of the methods fuzzy TOPSIS and fuzzy AHP were developed in Matlab© and applied to the selection of suppliers of a company in the automotive production chain. Five suppliers were evaluated regarding quality of conformance, cost, delivery, profile and relationship. The weight of the criteria and the performance of the suppliers were evaluated by specialist opinion from the studied company. The methods Fuzzy TOPSIS e Fuzzy AHP were compared in terms of ability to support the group decision, supplier qualification, final choice of suppliers, buying situations and modeling decisions under uncertainty. The efficiency of the methods with respect to computational complexity and the required user interaction was also compared. The comparative analysis shows that Fuzzy TOPSIS presents better than Fuzzy AHP performance, especially in scenarios in wich many alternatives are evaluated. Thus, Fuzzy TOPSIS is more flexible and appropriate than Fuzzy AHP to deal with supplier selection problem. This paper presents a new study, comparing the methods Fuzzy TOPSIS and Fuzzy AHP. As commented by Ertugrul and Karakasoglu (2008), a study such as this can contribute to the advance of knowledge, helping researchers and practitioners choosing more effective approaches to supplier selection.