Evolsys: um ambiente de configuração e análise de algoritmos evolutivos para sintonia da base de regras fuzzy do sistema de controle de um FMS

• Ano de defesa: 2015
• Autor(a) principal: Santana, Maykon Rocha
• Orientador(a): Morandin Júnior, Orides
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
• Palavras-chave em Português: Sistema de Manufatura Flexível; Sistemas Fuzzy; Algoritmos Genéticos; Algoritmos Evolutivos; Base de Regras Fuzzy
• Palavras-chave em Inglês: Flexible Manufacturing System; Fuzzy Systems; Genetic Algorithms; Evolutionary Algorithms; Fuzzy Rule Bases; Fuzzy Control Systems
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/8413

In recent years, companies have used Artificial Intelligence (AI) techniques to facilitate the decisionmaking process in manufacturing systems. The use of these techniques allows increased performance of Flexible Manufacturing System (FMS). The automation of the process using computational resources allows a deeper analysis of the system conditions, which sometimes result in a better decision taking. In this sense, the Fuzzy Logic has been engaged to carry out this task, because it has the characteristic of dealing easily with inaccurate information and encoding knowledge specialist in Fuzzy rules. However, as soon as the system complexity increases, the task of generating a Fuzzy Rule Base (FRB) appropriate to the proposed system becomes increasingly difficult. To assist this process of generation of the FRB, several techniques can be used and among them stand out the search technique called Evolutionary Algorithm (EA). The EA is used, for example, for tuning the FRB of the FMS through the reduction of the optimization variables values as Makespan or Tardiness. In the case of variable called Makespan, the tuning occurs when the EA generates an FRB that reduces the makespan values of a FMS. However, the construction of the EA that effectively generates a tuning FRB is not trivial. It is required to be in the process, the construction of various EA with different selection methods and different mutation rates among other settings until an appropriate EA for a given situation appears. Therefore, in this study we aim to build an environment configuration and performance analysis of EAs in order to define the tuning FRB of the Fuzzy Control System of an FMS, i.e., it is intended to investigate how the EA ideal parameter scenario used for tuning the FRB of the said control system. In this study, the used EA was an extension of Genetic Algorithm (GA). For implementing the proposal, an evolutionary system for configuration and analysis of this variant of the GA was created. In this system, entitled "EvolSys - Evolutionary System" parameters of the system as Number of Input Variables of FRB, Number of Output Variables of FRB, Population Size, Mutation Rate and the EA Crossover Rate, among others are configured and then, one FRB is generated. Using this, there is an EA analysis of the possibility for choosing a FRB that will provide the reduction of makespan in FMS. Consequently, through this study, we may conclude that the use of EAs in collaboration with Fuzzy system may become an important tool for turning the system responsibility to the sequences of an FMS operation. Accordingly, the environment created meets the configuration step and analysis of EAs.