Teaching to seasoned managers in MBE classes is challenging. While it’s important to bring new thoughts and ideas and not sound repetitive, it is necessary to provide a theoretical basis for experienced people with diverse backgrounds. One of the strategies I found to overcome these obstacles this week was to use a new analysis framework (in my case, Factory Physics concepts) to challenge their views about existing frames they already master.
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.
Arena Simulation is a well-known Discrete Event Simulation Software. However, if you are a power user you might want to extend your analysis beyond what Arena’s Process Analyzer offers. In this tutorial, I’ll guide you through the main functions of Arena2R package.
If you’re not an R user, fear not! Arena2R comes with an app you can use to explore your Arena Simulation data. All you’ll have to do is to Install R and R Studio, and run two commands in your R console.