Robust dual-response optimization
Yanikoglu,Ihsan den Hertog,Dick Kleijnen,J.P.C.
Yanikoglu,Ihsan
den Hertog,Dick
Kleijnen,J.P.C.
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.
Description
Date
2016
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Volume Title
Publisher
Research Projects
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Keywords
robust optimization, dual-response optimization, simulation optimization
Citation
Yanikoglu, I, den Hertog, D & Kleijnen, J P C 2016, 'Robust dual-response optimization', IIE Transactions: Industrial Engineering Research and Development, vol. 48, no. 3, pp. 298-312. https://doi.org/10.1080/0740817X.2015.1067737