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 the supplier management, it is necessary to compare the performances of two suppliers using linear pro les. In this study, the determination procedure for product acceptance was designed using the Exponentially Weighted Moving Average (EWMA) statistic based on the process-yield index applied to the linear pro les of two suppliers. The design parameters of the proposed plan were also determined to satisfy both the producer's and consumer's risks. The e ciency of the proposed sampling plan was compared to that of the sampling plan developed based on Wang's test statistic in terms of the sample size required for selecting a better supplier. To this end, a real example was presented to explain the proposed sampling plan.

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 this paper, a new nonparametric control chart based on two EWMA statistics is proposed for monitoring process deviation from the target value. The performance comparison of the proposed chart with the existing nonparametric EWMA sign control chart and a nonparametric CUSUM mean control chart are made by using out-of-control average run length. The simulation study showed the superiority of the suggested chart over the four other parametric and nonparametric charts. An empirical illustration is also provided for performance evaluation of the proposed chart. By comparing the proposed chart with the existing charts, it is concluded that the proposed chart is more sensitive in detecting a small shift in the process.