Estudo comparativo de diferentes representações cromossômicas nos algoritmos genéticos em problemas de sequenciamento da produção em job shop

• Ano de defesa: 2015
• Autor(a) principal: Módolo Junior, Valdemar
• Orientador(a): Pereira, Fabio Henrique
• Banca de defesa: Pereira, Fabio Henrique , Tolosa, Thiago Antonio Grandi de , Sassi, Renato José
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Nove de Julho
• Programa de Pós-Graduação: Programa de Pós-Graduação de Mestrado e Doutorado em Engenharia de Produção
• Departamento: Engenharia
• País: Brasil
• Palavras-chave em Português: representação cromossômica; operadores genéticos; sequenciamento da produção; job shop; algoritmos genéticos
• Palavras-chave em Inglês: chromosomal representation; genetic operators; production sequencing; job shop; genetic algorithms
• Área do conhecimento CNPq: ENGENHARIAS::ENGENHARIA DE PRODUCAO
• Link de acesso: http://bibliotecatede.uninove.br/handle/tede/1371

Among the optimization methods, the Genetic Algorithm (GA) has been producing good results in problems with high order of complexity, such as, for example, the production scheduling problem in job shop environment. The production sequencing problems must be translated into a mathematical representation, so that the AG can act. In this process we came up a problematic, the choice between different ways to represent the solution as some representations have limitations, how to present not feasible and / or redundant solutions. Therefore the aim of this study is to conduct a comparative study between different representations of the solution in the AG in production sequencing problems in job shop environments. Two representations of the solution were analyzed, the priority lists based and based on order of operations and compared with a binary representation, in the context of sequencing problem set defined by Lawrence (1984). The results were evaluated according to the total processing time (makespan), the computational cost and the proportion of generated feasible solutions. It was noticed that the representation of the solution based on order of operations, which produced 100% of feasible solutions, was the one that showed the best results although no convergence to the best known solution to every problem.