Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Quality Function Deployment Assistida por Otimização× | Desdobramento da Função Qualidade Robusto× | |
|---|---|---|
| Área | Delineamento experimental | Delineamento experimental |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1990s–2000s (QFD base: ~1966) | 2000s (robust extensions of QFD originating 1966) |
| Autor original≠ | Yoji Akao (QFD); optimization extensions by various researchers (1990s–2000s) | Extension of Yoji Akao's QFD (1966); robust adaptation by Fung, Kwong and others (early 2000s) |
| Tipo≠ | Integrated engineering design method | Hybrid quality-engineering planning method |
| Fonte seminal≠ | Akao, Y. (1990). Quality Function Deployment: Integrating Customer Requirements into Product Design. Productivity Press, Cambridge, MA. ISBN: 978-0915299416 | Fung, R. Y. K., Tang, J., & Tu, Y. (2002). Modeling of quality function deployment planning under resource allocation constraints. Computers & Industrial Engineering, 43(1–2), 313–328. link ↗ |
| Outros nomes | Optimization-integrated QFD, QFD with optimization, Mathematical programming QFD, OA-QFD | Robust QFD, Uncertainty-tolerant QFD, Fuzzy-robust QFD, Robust House of Quality |
| Relacionados | 4 | 4 |
| Resumo≠ | Optimization-assisted QFD extends the classic House of Quality framework by embedding mathematical optimization — linear programming, multi-objective optimization, or metaheuristics — directly into the QFD process. This allows engineers to simultaneously maximize customer satisfaction and minimize cost or resource constraints when setting target values for engineering characteristics, going beyond the largely subjective priority rankings of traditional QFD. | Robust Quality Function Deployment (Robust QFD) extends the classical House of Quality framework by explicitly modeling uncertainty and variability in customer requirements, perception ratings, and engineering correlation judgments. Instead of treating inputs as crisp single-point values, it applies fuzzy sets, interval analysis, or Taguchi-inspired robustness techniques to ensure that the resulting design targets remain stable and customer-satisfying even when inputs are imprecise or fluctuating. |
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