Mixed Control Chart Design Using the Integration of Genetic Algorithm and Monte Carlo Simulation

This research proposed a design of mixed control charts to monitoring the process quality based on attribute data together with variable data called a Mixed NE chart. The integration of Genetic Algorithm (GA) and Monte Carlo (MC) simulation are used to simultaneously assess the efficiency of a Mixed NE chart based on three different scenarios of the control limit coefficients (L-np,LEWMA (X) over bar -R). In this study, MC simulation is used to evaluate the maximum average of run length in case of in-control process ARL(0) while the GA is demanded to optimize the control limit coefficients that obtain the maximum ARL(0). In addition, the efficiency of the Mixed NE chart according to the process shifts are measured using the average of run length in case of out of control process (ARL(1)) and the extra quadratic loss(EQL). The results indicate that the Mixed NE chart performed well for small sample size(n), low smoothing constant (lambda(w), lambda(z)) and the optimal design of control limit coefficient is L*(np) > L*(EWMA (X) over bar -R). Moreover, the optimal design of the Mixed NE chart brings new important perspectives to achieve the most efficiency of control chart.