عنوان مقاله [English]
Control charts are widely used for process monitoring, but the ability of the traditional charts to detect the small shifts in the process is low. As a result, adaptive control charts have been introduced. Quick diagnose of process shifts and fewer defective products make these control charts more practical. In order to use the entire control charts, design of related parameters is needed. Because the sampling is used to ensure process stability, measurement error in control charts seems inevitable. There are many ways to reduce errors impact, one of which is multiple measurements of each product. In the present study, multiple measurement parameters are added to control chart design parameters (sample size, sampling interval, and control limits) and cost functions are modeled for control chart and variable sample size control chart. Thereafter, a numerical example of the presented model is considered, and eventually, Genetic Algorithm is used to calculate optimum values.Results showed that using adaptive control charts helps to notably reduce costs.Furthermore, due to the increase in the mean of quality characteristics in a constant measurement error variance, the number of required samples is decreased, and the number of multiple measurements for process monitoring is increased. In addition, through using Taguchi loss function for poor-quality products, an increase in distance from the mean will result in higher costs.This should be mentioned that as a result of growth in the rate of occurrences in assignable causes, the multiple measurements should increase.Sensitivity analysis is concerned in order to study the effect of input parameters values on the optimum values. According to the results, the number of multiple measurements for the quality characteristic of each item decreases when the slope increases.