An efficient elementary effect-based method for sensitivity analysis in identifying main and two-factor interaction effects

The analysis of engineering systems via computer-based models often confronts the intricacy of high-dimensional input spaces and notable interrelationships between inputs. This, in turn, necessitates the implementation of sensitivity analysis as a means of guiding reliability engineering. However, the query of effectively identifying significant effects, particularly interaction effects, remains unresolved. In this paper, we present a two-stage elementary effect-based sensitivity analysis method that effectively identifies main and interaction effects by fully utilizing the sequential characteristics of simulation experiments. Not only can the proposed method efficiently identify factors with important (a) main effects and (b) interaction and/or non-linear effects, but more importantly, it can discern the important two-factor interaction effects. Compared to state-of-the-art elementary effect-based methods, the proposed procedure can additionally identify specific interaction effects between two factors. Compared to traditional second-order elementary effect-based method, our method can achieve enormous computational savings without sacrificing statistical effectiveness. The Monte Carlo simulation experiments verify the feasibility and the real-world case study conducted to design a reliable cross-docking center manifests the robustness of the proposed method.