Statistical tools for simulation practitioners
Statistical tools for simulation practitioners
Simulation: a statistical perspective
Simulation: a statistical perspective
Five-stage procedure for the evaluation of simulation models through statistical techniques
WSC '96 Proceedings of the 28th conference on Winter simulation
Validation of trace-driven simulation models: regression analysis revisited
WSC '96 Proceedings of the 28th conference on Winter simulation
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Simulation models are often used to support decision making for problems with uncertain inputs and parameters. Three types of models are used: deterministic, risk, and uncertainty models. Risk models are popular with researchers, but can be used only when the joint probability distribution of the inputs and parameters is known. In many real-life situations, however, this is not the case. Uncertainty models are too restrictive for real-life situations. Therefore deterministic models are then used. The sensitivity of the results is often analyzed by changing one factor at a time or by simulating a few scenarios. This paper, however, shows that in case of uncertainty it might be better to apply design of experiments (DOE) in combination with regression metamodels.