Sensitivity analysis in discrete-event simulation using fractional factorial designs

This paper presents a sensitivity analysis of discrete-event simulation models based on a twofold approach formed by Design of Experiments (DOE) factorial designs and simulation routines. This sensitivity analysis aim is to reduce the number of factors used as optimization input via simulation. The advantage of reducing the input factors is that optimum search can become very time-consuming as the number of factors increases. Two cases were used to illustrate the proposal: the first one, formed only by discrete variable, and the second presenting both discrete and continuous variables. The paper also shows the use of the Johnson's transformation to experiments with non-normal response variables. The specific case of the sensitivity analysis with a Poisson distribution response was studied. Generally, discrete probability distributions lead to violation of constant variance assumption, which is an important principle in DOE. Finally, a comparison between optimization conducted without planning and optimization based on sensitivity analysis results was carried out. The main conclusion of this work is that it is possible to reduce the number of runs needed to find optimum values, while generating a system knowledge capable to improve resource allocation.