A multistage process consists of sequential stages where each stage is affected by its preceding stage, and it in turn affects the stage that follows. The process described in this article also has several input and response variables whose relationships are complicated. These characteristics make it difficult to optimize all responses in the multistage process. We modify a data mining method called the patient rule induction method and combine it with desirability function methods to optimize the mean and variance of multiresponse in the multistage process. The proposed method is explained by a step-by-step procedure using a steel manufacturing process example.

一个多阶段的过程由顺序的阶段组成，其中每个阶段都受其前一阶段的影响，进而又影响随后的阶段。本文中描述的过程还具有多个输入和响应变量，它们之间的关系很复杂。这些特征使得难以在多阶段过程中优化所有响应。我们修改了一种称为“患者规则归纳法”的数据挖掘方法，并将其与合意函数方法相结合，以优化多阶段过程中多响应的均值和方差。通过使用钢制造过程示例的分步过程介绍了提出的方法。