Estratégia de modelagem da tarefa de programação reativa da produção

• Ano de defesa: 2009
• Autor(a) principal: Montoro, Flavio Aldrovandi
• Orientador(a): Morandin Júnior, Orides
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
• País: BR
• Palavras-chave em Português: Inteligência artificial; Engenharia do conhecimento; Programação reativa da produção; Conhecimento tácito; Apoio à decisão; Representação do conhecimento
• Palavras-chave em Inglês: Knowledge engineering; Reactive production scheduling; Tacit knowledge; Decision-making; Knowledge representation
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufscar.br/handle/20.500.14289/483

Aiming at improving competitive advantage, organizations in general have been giving more attention to knowledge engineering, as well as its techniques and methods to acquire and represent knowledge already in place. This knowledge is sometimes not fully explored, or not explored properly. This approach is to develop a knowledge modeling strategy for reactive production scheduling, which focuses on tacit and explicit knowledge acquisition and representation. The knowledge representation aims at getting results closer to the organization reality and should also be comprehensible and easy to be validated by experts. To validate the proposed model, a computational system was developed to evaluate the model behavior under a specific domain, as well as to verify if the results are satisfactory. The model supports decision-making when unplanned events occur during the production process, enabling the possibility of evaluating greater knowledge to make the decision and also allowing the operator to make the decision without any expert support. The validation was performed in two steps; the first one, the computational system validation, was carried out by unit tests on every module, and after that, an integration test was performed. The second one, the proposed model validation, was verified in two ways. The first one was the validation using the industry use cases based on industry platform existing in the TEAR laboratory; the acceptance and understanding of the models by experts were verified using the same models already validated in a project between an industry and the TEAR laboratory.