An Efficient Design of Experiments Using RBD-Fast

Abstract

This research work deals with the efficient Design of Experiments (DoE) for the hybrid variant of the sensitivity analysis method RBD-FAST called Hyprid FAST RBD (HFR). RBD-FAST is a combination of the Random Balance Design (RBD) and the Fourier Amplitude Sensitivity analysis Test (FAST). Commonly, sensitivity analyses are used to determine whether input parameters have an influence on a target value or not. Currently, there is only little research to be found for the HFR method. The HFR method separates the input parameters into groups. Different constraints must be met for an optimal grouping. Theoretically, for every square number an optimal grouping exists, but only for squares of primes an optimal grouping is known to exist. An experiment with any number of input parameters needs as many samples as an experiment with the next higher square of a prime number as the number of input parameters. In this research work optimal groupings for squares of non-primes for the HFR method are found using a brute force algorithm.

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