Author
Yanıkoğlu, İhsan, Hertog, D. den, Kleijnen, J. P. C.
Publication Date
2016
Publication Place
-
Taylor & Francis
Subject
Robust optimization, Dual response optimization, Simulation optimization, Phi-divergence
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
1545-8830
Record ID
92e069c0-1f67-44fb-ac60-1b320d21bbe2
Library Location
Industrial Engineering
Date
2016
Sample Text
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.
DOI
10.1080/0740817X.2015.1067737
Cilt
48
