Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Plan factoriel fractionnaire assisté par optimisation× | Planification d'Expériences× | |
|---|---|---|
| Domaine | Plans d'expériences | Plans d'expériences |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1960s–1980s (D-optimality: Kiefer & Wolfowitz 1959; coordinate-exchange: Meyer & Nachtsheim 1995) | 1935 |
| Auteur d'origine≠ | A. C. Atkinson, A. N. Donev (optimality criteria); V. V. Federov (exchange algorithms) | Ronald A. Fisher |
| Type≠ | Optimal experimental design / computer-generated DOE | Experimental planning framework |
| Source fondatrice≠ | Atkinson, A. C., Donev, A. N., & Tobias, R. D. (2007). Optimum Experimental Designs, with SAS. Oxford University Press. ISBN: 978-0199296606 | Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd. link ↗ |
| Alias | optimal fractional factorial design, algorithmically optimized FFD, computer-aided fractional factorial design, D-optimal fractional factorial design | DOE, experimental design, factorial experimentation, planned experimentation |
| Apparentées≠ | 4 | 3 |
| Résumé≠ | Optimization-assisted fractional factorial design (OA-FFD) combines classical fractional factorial screening with algorithmic optimality criteria — such as D-, I-, or A-optimality — to construct experiment matrices that maximize statistical efficiency. Instead of relying solely on standard orthogonal-array tables, a computer algorithm selects the best subset of runs from a candidate set, enabling experimenters to handle irregular factor constraints, mixed factor types, and custom run sizes that standard tables cannot accommodate. | 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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