Covalidation of Dissimilarly Structured Models

A methodology is presented which allows comparison between models constructed under different modeling paradigms. Consider the following situation: Two models are constructed to study different aspects of the same system. One model simulates a fleet of aircraft moving a given combination of cargo and passengers from an onload point to an offload point. A second model is a linear programming model that optimizes the aircraft and route selection required for the same scenario. We develop a methodology to structure the comparison between large-scale models such as these. Models that compare favorably using this methodology are deemed covalid. Models that perform similarly under the same input conditions are covalid in a narrow sense. Models that are covalid (in this narrow sense) hold the potential to be used in an iterative fashion to improve the input (and thus, the output) of one another We prove that, under certain regularity conditions, this method of output/input crossflow converges, and if the convergence is to a valid representation of the real-world system, the models are covalid in a wide sense. Further, if one of the models has been independently validated, then we may effect a validation by association of the other model through this process

Sensitivity Analysis of a Large Scale Transportation Simulation Using Design of Experiments and Factor Analysis

This paper describes how design of eX1Jeriments and factor analysis were used to conduct sensitivity analysis on multivariate output from a large scale transportation simulation model. Specifically, this research focused on the sensitivity of airlift system performance to changes or errors in a list of transportation requirements. The general approach included perturbing a time-phased list of transportation requirements according to an experimental design and using a simulation model to estimate the airlift system performance response. We used factor analysis to reduce the dimensionality of the multivariate output data and to generate sensitivity plots, which proved to be valuable graphical tools for sensitivity analysis. Additionally, we identified how factor analysis can be used as a verification and validation tool for large stochastic simulation models.