Abstract
'Input uncertainty' refers to the simulation model risk caused by estimating input distributions from real-world data, and specifically the (usually unmeasured) variance in performance estimates that this introduces. We provide the first single-run method for quantifying input uncertainty, meaning that we derive our measure of input-uncertainty variance - both overall variance and the contribution to it of each input model - from the nominal experiment that the analyst would typically run using the estimated input models; other methods in the literature require additional diagnostic experiments. Application of our method is illustrated with two examples.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 249-259 |
| Number of pages | 11 |
| Journal | Journal of Simulation |
| Volume | 9 |
| Issue number | 3 |
| DOIs | |
| State | Published - Aug 16 2015 |
All Science Journal Classification (ASJC) codes
- Software
- Modeling and Simulation
- Management Science and Operations Research
- Industrial and Manufacturing Engineering