পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| অপ্টিমাইজেশন-সহায়ক ফ্র্যাকশনাল ফ্যাক্টোরিয়াল ডিজাইন× | পরীক্ষা নকশা× | |
|---|---|---|
| ক্ষেত্র | পরীক্ষামূলক নকশা | পরীক্ষামূলক নকশা |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 1960s–1980s (D-optimality: Kiefer & Wolfowitz 1959; coordinate-exchange: Meyer & Nachtsheim 1995) | 1935 |
| প্রবর্তক≠ | A. C. Atkinson, A. N. Donev (optimality criteria); V. V. Federov (exchange algorithms) | Ronald A. Fisher |
| ধরন≠ | Optimal experimental design / computer-generated DOE | Experimental planning framework |
| মৌলিক উৎস≠ | 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 ↗ |
| অপর নাম | optimal fractional factorial design, algorithmically optimized FFD, computer-aided fractional factorial design, D-optimal fractional factorial design | DOE, experimental design, factorial experimentation, planned experimentation |
| সম্পর্কিত≠ | 4 | 3 |
| সারসংক্ষেপ≠ | 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. |
| ScholarGateডেটাসেট ↗ |
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