Performance evaluation of multivariate non-normal process using metaheuristic approaches

Multivariate process performance indices generally rely on the assumption that the process follow normal distribution but in practice its non-normal with correlated characteristics patterns. This paper proposes two metaheuristic-based approaches to fit Burr distribution to such data; a single candidate model-based approach using a Simulated Annealing (SA) technique and a population based approach using a constraint-based Evolutionary Algorithm (EA). The fitted Burr distribution is then used to estimate the proportion of Non-conforming (PNC) which is then used to assess the efficacy of the proposed methods. The metaheuristic approaches are used to fit an appropriate Burr distribution to individual Geometric distance variables. Empirical performances of the proposed methods have been evaluated on real industrial data set using PNC criterion. Experimental results demonstrate that the new approach perform well than the existing.