MR-CART: Multiresponse optimization using a classification and regression tree method

Abstract

The conventional approach for optimizing multiresponse is fitting multiple response surface models and then analyzing them to obtain optimal settings for the input variables. However, it is difficult to obtain reliable response surface models when dealing with large amounts of data. In this article, a new approach to multiresponse optimization based on a classification and regression tree method is presented. Desirability functions are employed to simultaneously optimize the multiple responses. The case study of steel manufacturing company with large amounts of data shows that the proposed method obtains an optimal region in which multiple responses are simultaneously optimized.

Keywords:

• Big data
• CART
• desirability function
• manufacturing process optimization
• multiresponse optimization

Additional information

Funding

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07049412). Also, this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1A2C1007834).

Notes on contributors

Dong-Hee Lee

Dong-Hee Lee is an Associate Professor in the College of Interdisciplinary Industrial Studies at Hanyang University in Korea. He received his BS and PhD in Industrial and Management Engineering from Pohang University of Science and Technology (Korea) in 2006 and 2011, respectively. He worked as a senior researcher in quality team of semiconductor division at Samsung Electronics for 4 years and received CRE (certified reliability engineer) from ASQ (American Society for Quality). His research interests include quality engineering methods such as statistical process control, design of experiments, statistical analysis and so on. He has published several research papers about multiresponse surface optimization.

So-Hee Kim

So-Hee Kim is a MS candidate student in the College of Interdisciplinary Industrial Studies at Hanyang University in Korea. Her research interests include statistical quality control, design of experiments, and response surface methodology and so on.

Eun-Su Kim

Eun-Su Kim is a MS candidate student in the College of Interdisciplinary Industrial Studies at Hanyang University in Korea. Her research interests includes quality engineering methods based on artificial intelligence methods.

Kwang-Jae Kim

Kwang-Jae Kim is a Professor in the Department of Industrial and Management Engineering at Pohang University of Science and Technology, Korea. He earned his BS in Industrial Engineering in 1984 from Seoul National University, Korea, his MS in Industrial Engineering in 1986 from Korea Advanced Institute of Science and Technology, Korea, and his PhD in Management Science in 1993 from Purdue University. His research interests include quality assurance in product and process design, new product/service development, and service engineering. He is a member of ASQ, IIE, and INFORMS.

Zhen He

Zhen He is a professor in the College of Management and Economics, Tianjin University. He is also the Six Sigma consultant of Company T. He is the recipient of Outstanding Research Young Scholar Award of the National Natural Science Foundation of China. He has published more than 100 papers and coauthored five books. He is the chairman of the Six Sigma Expert Steering Committee of China Association for Quality. His research interests focus on quality management, statistical quality control, DOE and Six Sigma management