Recent developments in metamodel based robust black-box simulation optimization: An overview
Paper
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the real world of engineering problems, in order to reduce optimization
costs in physical processes, running simulation experiments in the
format of computer codes have been conducted. It is desired to improve
the validity of simulation-optimization results by attending the source
of variability in the model’s output(s). Uncertainty can increase
complexity and computational costs in Designing and Analyzing of
Computer Experiments (DACE). In this state-of the art review paper, a
systematic qualitative and quantitative review is implemented among
Metamodel Based Robust Simulation Optimization (MBRSO) for black-box and
expensive simulation models under uncertainty. This context is focused
on the management of uncertainty, particularly based on the Taguchi
worldview on robust design and robust optimization methods in the class
of dual response methodology when simulation optimization can be handled
by surrogates. At the end, while both trends and gaps in the research
field are highlighted, some suggestions for future research are
directed.
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