A tutorial on design of experiments for simulation modeling

Simulation models often have many input factors, and determining which ones have a significant impact on performance measures (responses) of interest can be a difficult task. The common approach of changing one factor at a time is statistically inefficient and, more importantly, is very often just i...

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Bibliographic Details
Main Author: Law, Averill M.
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
Companies
Computational modeling
Context modeling
Mathematical model
Numerical models
Tutorials
Online Access:Request full text
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Description
Summary:Simulation models often have many input factors, and determining which ones have a significant impact on performance measures (responses) of interest can be a difficult task. The common approach of changing one factor at a time is statistically inefficient and, more importantly, is very often just incorrect, because for many models factors interact to impact on the responses. In this tutorial we present an introduction to design of experiments specifically for simulation modeling, whose major goal is to determine the important factors often with the least amount of simulating. We discuss classical experimental designs such as full factorial, fractional factorial, and central composite followed by a presentation on Latin hypercube designs, which are designed for the complex, nonlinear responses typically associated with simulation models.
ISSN:1558-4305
DOI:10.1109/WSC.2017.8247814