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Optimalizace plně faktoriálního uspořádání pomocí optimalizace×Vícenásobný plně faktoriální design×
OborPlánování experimentůPlánování experimentů
RodinaProcess / pipelineProcess / pipeline
Rok vzniku1980s–1990s (formalized with desirability functions by Derringer & Suich, 1980)1950s–1980s
TvůrceIntegrated from D. C. Montgomery (DoE) and classical optimization literatureDouglas C. Montgomery (factorial framework); Derringer & Suich (multi-response desirability optimization)
TypHybrid experimental-optimization workflowExperimental design with multi-objective optimization
Původní zdrojMontgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443
Další názvyOA-FFD, full factorial with optimization, full factorial design with response optimization, DoE-optimization hybridMRFFD, multi-response FFD, multiple-response full factorial, multi-objective full factorial design
Příbuzné33
ShrnutíOptimization-assisted full factorial design is a structured engineering workflow that runs a complete full factorial experiment — covering every combination of factor levels — and then applies a formal optimization method to identify the factor settings that best satisfy one or more performance targets. It combines the exhaustive data coverage of full factorial design with numerical or analytical optimization to turn experimental results into actionable optimal configurations.Multi-response full factorial design extends the classic full factorial experiment by measuring and jointly optimizing two or more response variables at the same time. Every combination of all factor levels is tested, providing complete main-effect and interaction information for each response. A desirability function or Pareto-front approach then reconciles competing responses into a single optimal factor setting, making this the method of choice when engineering or process goals involve trade-offs among several quality characteristics simultaneously.
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ScholarGatePorovnat metody: Optimization-assisted full factorial design · Multi-response full factorial design. Získáno 2026-06-18 z https://scholargate.app/cs/compare