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| 최적화 지원 실험 설계× | 실험계획법× | |
|---|---|---|
| 분야 | 실험설계 | 실험설계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1980 (desirability approach); broader integration through 1990s–2000s | 1935 |
| 창시자≠ | Derringer & Suich (desirability function); extended by Myers, Montgomery, and Anderson-Cook | Ronald A. Fisher |
| 유형≠ | Hybrid experimental-optimization method | Experimental planning framework |
| 원전≠ | Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. DOI ↗ | Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd. link ↗ |
| 별칭 | OA-DoE, DoE with optimization, optimization-integrated DoE, multi-objective experimental optimization | DOE, experimental design, factorial experimentation, planned experimentation |
| 관련≠ | 4 | 3 |
| 요약≠ | 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. | Design of Experiments (DOE) is a systematic framework for planning, conducting, and analyzing controlled experiments to determine how multiple input factors simultaneously affect one or more responses. Introduced by Ronald A. Fisher in 1935, DOE allows researchers and engineers to identify causal relationships, quantify factor effects, and find optimal settings efficiently — using far fewer runs than one-factor-at-a-time approaches. It is foundational in engineering, manufacturing, agriculture, and applied sciences. |
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