Sensitivity analysis framework for large and complex simulation models

Authors:
Ghaith Rabadi;Shannon Bowling;Charles Keating;Resit Unal
Affiliations:
Old Dominion University, Norfolk, Virginia;Old Dominion University, Norfolk, Virginia;Old Dominion University, Norfolk, Virginia;Old Dominion University, Norfolk, Virginia
Venue:
SCSC '09 Proceedings of the 2009 Summer Computer Simulation Conference
Year:
2009

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Abstract

In this paper, a framework for conducting Sensitivity Analysis (SA) on large and complex simulation models is introduced. The framework consists of components that are designed to make the SA a systematic process that is easy to manage and follow by simulation analysts and practitioners. Unlike local SA (one-variable-at-a-time SA), the method presented here is variance-based and it is rooted in the field of Design of Experiments (DoE) where Input Variables are varied and Output Variables are measured. Based on the DoE results, a risk scoring system is developed to identify the sensitivity of the Input Variables, and as a result classify them into High, Medium, and Low risk variables. As such, decision makers can be aware of the most sensitive high-risk input variables in a simulation model to ensure they understand the value of data reliability in their model inputs.