Author/Authors :
Hosseini، Raziyeh نويسنده Dept. of Statistic, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran , , Amirzadeh، Vahid Amirzadeh نويسنده Dept. of Statistic, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran , , Yaghoobi، Mohammad Ali نويسنده Dept. of Mathematics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran , , Mirzaie، Hojjat نويسنده Dept. of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran ,
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.
