Optimization of univariate and multivariate exponentially weighted moving-average control charts using genetic algorithms

Exponentially weighted moving-average (EWMA) and multivariate EWMA (MEWMA) process control charts can be applied to detect small changes in statistical process control efficiently. This paper presents a software program developed in Windows environment for the optimal design of the EWMA and MEWMA chart parameters, to protect the process in the case of shifts of given size. Optimization has been done using genetic algorithms.