Traditional statistical process control for variable data often involves the use of a separate mean and a standard deviation chart. Several proposals have been published recently, where a single (combination) chart that is simpler and may have performance advantages, is used. The assumption of normality is crucial for the validity of these charts. In this article, a single distribution-free She...

Recent studies have shown that the variable sampling interval (VSI) scheme helps practitioners detect process shifts more quickly than the classical scheme (FRS). In this paper, the economically and statistically optimal design of the VSI T2 control chart for monitoring the process mean vector is investigated. The cost model proposed by Lorenzen and Vance (1986) is minimized through a genetic a...

The principal function of a control chart is to help management distinguish different sources of variation in a process. Control charts are widely used as a graphical tool to monitor a process in order to improve the quality of the product. Chen and Hsieh (2007) have designed a T2 control chart using a Variable Sampling Size and Control limits (V SSC) scheme. They have shown that using the V SSC...

Distribution-free or nonparametric control charts are used for monitoring the process parameters when there is a lack of knowledge about underlying distribution. In this paper, we investigate single distribution-free triple exponentially weighted moving average chart based on Lepage statistic (referred as TL chart) simultaneously shifts in unknown location and scale univariate continuous The de...

Control charts are one of the most important tools in statistical process control that lead to improve quality processes and ensure required quality levels. In traditional control charts, all data should be exactly known, whereas there are many quality characteristics that cannot be expressed in numerical scale, such as characteristics for appearance, softness, and color. Fuzzy sets theory is a...