Method evidence record
Optimization-assisted design of experiments
Optimization-assisted design of experiments (OA-DoE) couples a structured experimental plan with a mathematical optimization engine to locate factor settings that simultaneously satisfy multiple response objectives. Rather than stopping at fitting a response surface model, the analyst applies desirability functions, genetic algorithms, or other optimizers to the fitted model to identify the global or near-global optimum across all responses of interest.
Evidence statusSources recorded, not reviewedBibliographic sources are present. Claim-level evidence review has not been performed.
Source record
Recorded
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
Optimization-Assisted Design of Experiments
Taxonomic method recordprocess-pipelineexperimental-design
Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley. · ISBN 978-1118916018
Open full method2 recorded citations, copied from the method source record.
Curated claims
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Related methods
Suggested4
Generated from the method graph and shown as machine-suggested relations. No evidence claim is inferred.
- Taxonomic bucketBox-Behnken Design
machine-suggestedRelational suggestion, not evidence.
- Taxonomic bucketCentral Composite Design
machine-suggestedRelational suggestion, not evidence.
- Taxonomic bucketDesign of experiments
machine-suggestedRelational suggestion, not evidence.
- Used in the same domainResponse Surface Methodology
machine-suggestedRelational suggestion, not evidence.