Designs for screening experiments usually include factors with two levels only. Adding a few four-level factors allows for the inclusion of multi-level categorical factors or quantitative factors with possible quadratic or third-order effects. Three examples motivated us to generate a large catalog of designs with two-level factors as well as four-level factors. To create the catalog, we considered three methods. In the first method, we select designs using a search table, and in the second method, we use a procedure that selects candidate designs based on the properties of their projections into fewer factors. The third method is actually a benchmark method, in which we use a general orthogonal array enumeration algorithm. We compare the efficiencies of the new methods for generating complete sets of non-isomorphic designs. Finally, we use the most efficient method to generate a catalog of designs with up to three four-level factors and up to 20 two-level factors for run sizes 16, 32, 64, and 128. In some cases, a complete enumeration was infeasible. For these cases, we used a bounded enumeration strategy instead. We demonstrate the usefulness of the catalog by revisiting the motivating examples.
翻译:筛选实验的设计通常只包括两个层次的因素。加上几个四级因素,可以包括多级绝对因素或数量因素,并可能具有四级或第三级效应。三个例子促使我们产生了大型设计目录,其中含有两个层次的因素和四个层次的因素。为了创建目录,我们考虑了三种方法。在第一个方法中,我们使用搜索表格选择设计,在第二个方法中,我们使用一种程序,根据预测的特性选择候选人设计,将其分为较少的因素。第三个方法实际上是一种基准方法,其中我们使用一般的或横向的数组查点算法。我们比较了产生全套非线形设计的新方法的效率。最后,我们使用最有效率的方法来生成一个设计目录,其中最多有三个四个层次的因素和最多20个两个层次的因素,用于运行大小16、32、64和128。在某些情况下,完全的查点是不可行的。对于这些情况,我们采用了一个封闭的查点战略。我们用重新研究这些例子来证明该目录的效用。我们用重新研究这些例子来证明该目录的效用。</s>