Bayesian robust parameter design for ordered response

Shijuan Yang, Jianjun Wang and Yiliu Tu

International Journal of Production Research, 2022, vol. 60, issue 12, 3630-3650

Abstract: Due to the nature of the quality characteristics, or there is no instrument available to measure the characteristics of interest, ordered data, e.g. 1 (poor), 2 (satisfactory), 3 (good), and 4 (excellent), often appears in industrial processes. Methods commonly used for continuous or categorical quality characteristics are not appropriate for modelling and optimising such quality characteristics. This motivated us to develop a more useful approach to address the variable selection, model construction, and process optimisation for the ordered response. Specifically, Bayesian Lasso is incorporated into the framework of the response surface model to simultaneously perform variable selection and model estimation. The relationship between the probability of the response falls into a specific category and significant factor effects are established by introducing a latent variable. The desirability function, which is commonly used for multi-objective optimisation for quantitative responses, is extended to process optimisation for the ordered response. A numerical example and an industrial case are used to validate the effectiveness of the proposed method. The performance studies of the proposed method show that our method is more competitive than existing methods.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2021.1930235 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:60:y:2022:i:12:p:3630-3650

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2021.1930235

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().