An economic design of double sampling X¯ charts for correlated data using genetic algorithms

Double sampling X¯ control chart (DS) which is a Shewhart-type chart can reduce sample size and detect small process shift fast. In process monitoring of real industry, observations may be interdependent and correlated, and an original DS design will occur high cost for the wrong determination of the process state. In this study, an economic design model of DS is developed based on Yang and Hancock’s assumption of correlation and Lorenzen and Vance’s cost model to determine sample size, sampling interval, and coefficients of control limits and warring limits. The genetic algorithms (GAs) are applied to solve this economic design model of DS for the determination of the optimal parameters. A real example of IC packaging process is given to illustrate the model application. Sensitivity analysis shows the influence of different model parameters on the DS chart designs.