Large factorial designs for product engineering and marketing research applications
Large factorial designs for product engineering and marketing research applications
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DOI：
10.1198/004017004000000653
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发表时间：
2005-05-01
期刊：
影响因子：
2.5
通讯作者：
Tobias, RD
中科院分区：
文献类型：
--
作者：
Kuhfeld, WF;Tobias, RD
我们提出了一种结合组合和启发式优化方法的算法，用于生成 D 高效因子设计。其中包括一种可以自动构建超过 115,000 个正交阵列的方法，其中至少为每个已知规范构建一个少于 144 次运行的阵列。该算法尝试各种初始化和迭代方法，选择最有希望的方法，然后对所选方法应用更多的计算工作。该算法可以制作正交和近正交阵列，并且可以适应限制、交互和大型设计。使用需要最少的专业知识和输入。讨论了优化产品设计、选择建模和营销研究领域的应用。
We propose an algorithm that incorporates both combinatorial and heuristic optimization methods for generating D-efficient factorial designs. This includes an approach that can automatically construct more than 115,000 orthogonal arrays, including at least one array for every known specification with fewer than 144 runs. The algorithm tries various initialization and iteration methods, chooses the most promising method, and then applies more computational effort with the chosen method. The algorithm can make orthogonal and nearly-orthogonal arrays, and it can accommodate restrictions, interactions, and large designs. Usage requires minimal expertise and input. Applications in the area of optimal product design, choice modeling, and marketing research are discussed.