Setup adjustment for asymmetric cost functions under unknown process parameters

Zilong Lian, Arda Vanli, Enrique del Castillo

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

This paper presents a Bayesian approach for the optimal control of a machine that can experience setup errors assuming an asymmetric off-target cost function. It is assumed that the setup error cannot be observed directly due to presence of measurement and part-to-part errors, and it is further assumed that the variance of this error is not known a priori. The setup error can be compensated by performing sequential adjustments of the process mean based on observations of the parts produced. It isshown how the proposed method converges to the optimal (known variance) trajectory, recovering from a possibly biased initial variance estimate. Simulations results are presented to show the small sample behavior of the proposed method under two types of asymmetric off-target cost functions: a constant asymmetric and a quadratic asymmetric cost function.

Original languageEnglish (US)
Pages (from-to)471-489
Number of pages19
JournalQuality Technology and Quantitative Management
Volume11
Issue number4
DOIs
StatePublished - Jan 1 2014

All Science Journal Classification (ASJC) codes

  • Business and International Management
  • Industrial relations
  • Management Science and Operations Research
  • Information Systems and Management
  • Management of Technology and Innovation

Fingerprint Dive into the research topics of 'Setup adjustment for asymmetric cost functions under unknown process parameters'. Together they form a unique fingerprint.

  • Cite this