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Activity Number: 481 - Modeling, Analysis, and Assessment
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: Quality and Productivity Section
Abstract #329249 Presentation
Title: Factor Selection and Level Grouping with Applications to Golden Path Determination
Author(s): Nan-Jung Hsu*
Companies: National Tsing Hua University
Keywords: group lasso ; Poisson regression; regularization; stochastic gradient descent
Abstract:

Finding the golden path in a production process is a critical issue for intelligent manufacturing. This work solves this problem for a specific case that the production quality is measured by the failure counts and the factors relevant to the production quality all belong to categorical variables. Traditional approaches often identify the important factors (tools) affecting the yield of the process first, and then determine the best production path subject to the maximal mean yield, called the golden path. This study further takes into account the group patterns of the tool effects to provide a more flexible solution for the golden path in practice. To achieve this goal, we developed a penalized likelihood approach in a generalized linear model framework for factor selection and estimation under which the important factors are identified with equivalent levels grouped simultaneously. The effectiveness of the proposed method is illustrated via a simulation study and a manufacturing data set.


Authors who are presenting talks have a * after their name.

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