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| Pełne projektowanie czynnikowe wspomagane symulacją× | Projektowanie Doświadczeń× | |
|---|---|---|
| Dziedzina | Planowanie eksperymentów | Planowanie eksperymentów |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1990s–2000s (simulation-DOE integration formalized) | 1935 |
| Twórca≠ | Montgomery (DOE foundations); Kleijnen (simulation DOE formalization) | Ronald A. Fisher |
| Typ≠ | Experimental design with computer simulation | Experimental planning framework |
| Źródło pierwotne≠ | Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119113478 | Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd. link ↗ |
| Inne nazwy | SA-FFD, computer simulation full factorial, virtual full factorial design, simulation-based full factorial DOE | DOE, experimental design, factorial experimentation, planned experimentation |
| Pokrewne≠ | 4 | 3 |
| Podsumowanie≠ | Simulation-assisted full factorial design integrates full factorial design of experiments (DOE) with computer simulation models — such as discrete-event simulation, finite element analysis, or Monte Carlo methods — to systematically explore every combination of factor levels and quantify their effects on system responses. It enables comprehensive experimentation in contexts where physical trials would be costly, dangerous, or infeasible. | 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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