Since the advent of big data,the identification of high-dimensional interaction effects between factors has become an active research area.To tackle the problem,this paper proposes a new two-stage interaction-based EMM method(IEMM)by extending the efficient Morris method-based framework(EMM).IEMM makes full use of the sequential characteristics of computers.Not only can IEMM efficiently identify factors with significant main effects and interaction and/or non-linear effects,but it can also further discern the significant two-way interaction effects(TIE).Monte Carlo simulation results show that IEMM can correctly identify TIEs that EMM cannot recognize.Compared to the traditional interaction-based MM(IMM),IEM-M has higher computational efficiency in TIE recognition.The real-world logistics case shows that IEMM is feasible and robust to identify TIEs and can achieve computational savings without sacrificing statistical effectiveness.
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