Simulation Metamodeling - building and using surrogate models that can approximate results from more complicated simulation models - is an interesting approach to analyze results from complicated, computationally expensive simulation models. Metamodels are useful because they can yield good approximations of the original simulation model response variables using less computational resources. For an introduction to Metamodeling, refer to (Barton 2015).
To my knowledge, no Discrete-Event Simulation (DES) software provides metamodeling capabilities, and guidance on how to actually execute metamodeling is scarce.

This is part 1 of a series of posts in which I will explore the utility of using metamodels to make sense of (and possibly optimizing) simulation models.
If you used simulation modeling on a real project, you might be familiar with this fictional story:
You spent long hours building and refining your simulation model (eg.: a Discrete Event Model). Hopefully, you are confident that it can yield reliable results.

© 2018 · Built with R and blogdown. Powered by the Academic theme for Hugo.