Approximating the step change point of the process fraction non conforming using genetic algorithm to optimize the likelihood function

Abstract

Control charts are standard statistical process control (SPC) tools for detecting assignable causes. These charts trigger a signal when a process gets out of control but they do not indicate when the process change has begun. Identifying the real time of the change in the process, called the change point, is very important for eliminating the source(s) of the change. Knowing when a process has begun to change simplifies the identification of the special cause and consequently saves time and expenditure. This study uses genetic algorithms (GA) with optimum search features for approximately optimizing the likelihood function of the process fraction nonconforming. Extensive simulation results show that the proposed estimator outperforms the Maximum Likelihood Estimator (MLE) designed for step change regarding to speed and variance.