Determining the optimal parameters of the p-chart using genetic algorithm

A control chart is a crucially important tool in statistical process control that is essentially used to determine when a process remains in-control vis-a-vis when a process does not remain in-control. A process is said to be in-control if it is under the influence of chance or common causes of variation alone. Otherwise, when some assignable or special causes of variation remain present, the process is said to be out-of-control. The sample size, sampling interval, and control limits' multiplier are the important parameters required to design a control chart. In this article, we have proposed an economic statistical design of the p-chart to optimize the expected cost per cycle (C E ). Genetic Algorithm (GA) has been used to solve the proposed economic statistical design of the p-chart. The proposed approach is demonstrated with the help of two numerical examples and the corresponding results are found to be quite encouraging for minimizing C E when compared to another pertinent model given in the literature. The utilitarian value of this article can be found while implementing the ``control phase" in Six Sigma under the broader gamut of industrial engineering. The sensitivity analysis has also been carried out to investigate the influence of changes in input parameters on the output parameters for the proposed model.