Publicação
Maintenance decision making
| Resumo: | In any management process, decision making assumes a very important dimension. Complex systems are commonly fed with large amounts of data that are quickly made available to experts and industrial engineers who, in most cases, are not provided with adequate decision support tools. Therefore, the quality of their decisions heavily relies on their our quality and experience, making the complex systems management planning, particularly in maintenance planning, a very difficult and subjective process, by tendentially diverting analysts from the main decisional aspects. In order to overcome these difficulties and subjectivities, this paper purposes a set of methodological guidelines based on fuzzy set theory to be applied in the planning processes, leading to optimized and more realistic results. |
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| Autores principais: | Carvalho, Mariana Teixeira Baptista de |
| Outros Autores: | Nunes, Eusébio P.; Telhada, José |
| Assunto: | Maintenance planning fuzzy decision making fuzzy set theory uncertainty |
| Ano: | 2012 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | In any management process, decision making assumes a very important dimension. Complex systems are commonly fed with large amounts of data that are quickly made available to experts and industrial engineers who, in most cases, are not provided with adequate decision support tools. Therefore, the quality of their decisions heavily relies on their our quality and experience, making the complex systems management planning, particularly in maintenance planning, a very difficult and subjective process, by tendentially diverting analysts from the main decisional aspects. In order to overcome these difficulties and subjectivities, this paper purposes a set of methodological guidelines based on fuzzy set theory to be applied in the planning processes, leading to optimized and more realistic results. |
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