The exponentially weighted moving average (EWMA) and DOUBLE EWMA are two basic control charts in the sensitive manufacturing process such as semiconductor manufacturing. Optimal value for the weight parameters of these control charts are important for evaluation of their performance. In this paper we have used multi criteria decision making (MCDM) and goal programming (GP) models to reduce bias and standard deviation of statistics of EWMA and DOUBLE EWMA control charts based on decision maker openion. The proposed model as a Engineering Process Control (EPC) tools can be used for optimal adjastment of sensitive process.

In this paper we present a method for optimal design of combined EWMA-EWMA control scheme. Shewhart control chart, introduced by Walter Shewhart, only takes into account present information of the process, so it is insensitive in detecting small shifts. EWMA and CUSUM control charts are good choices for detecting small shifts. A combined control scheme monitors the process for large and small shifts simultaneously. The purpose of this paper is to develop a computer program for optimal design of combined EWMA-EWMA control scheme based on multiple objective optimization problems for detecting large and small shifts simultaneously.

Variance of EWMA Statistic is needed when control chart is used. In literatures, a relation is used for computation of this variance which is not exact. We show that this variance is less than the exact value. This paper first derives the exact variance of the EWMA statistic for independent observations. Then through the comparison of this new variance and old variance, the effect of exact variance on control limits of EWMA control chart is examined.

The mixed EWMA-CUSUM and CUSUM-EWMA control charts are among the control charts provided in recent years by combining two Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) charts for efficient monitoring of the process mean. In this paper, these mixed control charts are extended using new median ranked set sampling and DOUBLE ranked set sampling procedures. The performance of the proposed mixed control charts is evaluated through extensive Monte Carlo simulations in terms of average run length criterion. The results demonstrate that the proposed charts outperform similar ones in detecting different shifts in the process mean. Furthermore, a real dataset is also presented for explaining the implementation of the proposed control charts.

In the last few years, Run-to-Run (R2R) control techniques have been developed and used to control various processes in industries. These techniques combine response surface, statistical process control, and feedback control techniques. The R2R controller consists of a linear regression model that relates input variables to output variables using Exponentially Weighted Moving Average (EWMA). In this paper, we have developed a R2R controller model based on quality costs. The model consists of finding optimum weight of EWMA procedure in R2R controllers with respect to conformities and nonconformities costs. The validity and performance of the developed model were tested using a real case study in an optic industry application.  

In this paper, we propose new Shewhart-EWMA (SEWMA) and Shewhart-CUSUM (SCUSUM) control charts using the varied L ranked set sampling (VLRSS) for monitoring the process mean, namely the SEWMA-VLRSS and SCUSUM-VLRSS charts. The run length characteristics of the proposed charts are computed using extensive Monte Carlo simulations. The proposed charts are compared with their existing counterparts in terms of the average and standard deviation of run lengths. It is found that, with perfect and imperfect rankings, the SEWMA-VLRSS and SCUSUM-VLRSS charts are more sensitive than their analogous charts based on simple random sampling, ranked set sampling (RSS) and median RSS schemes. A real dataset is also used to explain the implementation of the proposed control charts.