Simulation Experiment Design and Analysis: Session II
Getting More from Your Simulation Model, Faster
Have you been troubled by any of the following questions?
- Is there much uncertainty about the conclusions of my simulation study due to uncertainty about the model’s input parameters?
- How can I calibrate my model’s input parameters so the model yields realistic behavior? • How can I use the model to find an optimal decision?
- Which of my model’s parameters are important, in the sense that reasonable changes to the parameters yield large changes in the model’s output?
- I’ve already done many simulation runs, with different values for the parameters.
- How can I approximate the model’s output for new values of the parameters without running a new simulation experiment? (I don’t want to wait for the experiment to run or spend more processor-hours.)
This series of two seminars provides an overview of methods to answer these questions and enhance your simulation-based research. Computational efficiency will be emphasized; the methods are applicable even to computationally intensive simulations with many parameters.
Jeremy Staum is Associate Professor and Director of Graduate Studies in the Dept. of Industrial Engineering & Management Sciences, Northwestern University. His research focuses on advanced methods of simulation modeling. He is on mini-sabbatical at Argonne in the Decision and Information Sciences Division for the fall quarter, as part of the Northwestern-Argonne Institute Mini-Sabbatical Program.