Robust dual-response optimization

Research output: Contribution to journalArticleScientificpeer-review

16 Citations (Scopus)

Abstract

This article presents a robust optimization reformulation of the dual-response problem developed in response surface methodology. The dual-response approach fits separate models for the mean and the variance and analyzes these two models in a mathematical optimization setting. We use metamodels estimated from experiments with both controllable and environmental inputs. These experiments may be performed with either real or simulated systems; we focus on simulation experiments. For the environmental inputs, classic approaches assume known means, variances, or covariances and sometimes even a known distribution. We, however, develop a method that uses only experimental data, so it does not need a known probability distribution. Moreover, our approach yields a solution that is robust against the ambiguity in the probability distribution. We also propose an adjustable robust optimization method that enables adjusting the values of the controllable factors after observing the values of the environmental factors. We illustrate our novel methods through several numerical examples, which demonstrate their effectiveness.
Original languageEnglish
Pages (from-to)298-312
JournalIIE Transactions: Industrial Engineering Research and Development
Volume48
Issue number3
DOIs
Publication statusPublished - 2016

Keywords

  • robust optimization
  • dual-response optimization
  • simulation optimization

Fingerprint Dive into the research topics of 'Robust dual-response optimization'. Together they form a unique fingerprint.

Cite this