An enhancement of DSI X̄ control charts using a fuzzy-genetic approach

A traditional control chart used to monitor a process draws the process data at a fixed sampling rate, while a variable sampling interval (VSI) control chart varies the sampling rate as a function of on-line process data. In such a sampling policy, a higher sampling rate is adopted when there is suspicion of a change in a process. Therefore, it is able to detect the process change faster than traditional control chart, and thus has been much accepted for use. Nevertheless, the binary suspicious grade used in VSI policy to specify the sampling rate is not detailed enough to explain the acquired information from process data. As a result, this paper aims to refine the suspicious grade and sampling interval lengths to increase the detection ability of VSI charts. This study first establishes a composition function on two sides of the control chart by introducing the concept of fuzzily suspicious grade to specify the sampling rate. Then, genetic algorithms (GAs) is used to adjust the values of the parameters in this composition function to enhance the dual-sampling-interval (DSI) charts-one type of the VSI charts in common use-in terms of average time to signal (ATS) for process mean shift. In addition, some statistical properties of the enhanced DSI charts as well as performance comparison to traditional DSI charts are provided and analysed.