Screening for the Important Factors in Large Discrete-Event Simulation Models: Sequential Bifurcation and Its Applications

Screening in simulation experiments to find the most important factors, from a very large number of factors, is discussed. The method of sequential bifurcation in the presence of random noise is described and is demonstrated through a case study from the mobile telecommunications industry. The case study involves 92 factors and three related, discrete-event simulation models. These models represent three supply chain configurations of varying complexity that were studied for an Ericsson factory in Sweden. Five replicates of observations from 21 combinations of factor levels (or scenarios) are simulated under a particular noise distribution, and a shortlist of the 11 most important factors is identified for the most complex of the three models. Various different assumptions underlying the sequential bifurcation technique are discussed, including the role of first- and second-order polynomial regression models to describe the response, and knowledge of the directions and relative sizes of the factor main effects.