Abstract
Economic design of multivariate exponentially weighted moving average (MEWMA) control charts for monitoring the process mean vector involves determining economically the optimum values of the three control parameters: the sample size, the sampling interval between successive samples, and the control limits or the critical region of the chart. In the economic-statistical design, constraints (including the requirements of type I error probability and power) are added such that the statistical property of the chart is satisfied. In this paper, using the multivariate Taguchi loss approach, the Lorenzen–Vance (Technometrics 28:3-10, 1) cost function of implementing the control chart is extended to include intangible external costs along with the in-control average run length (ARL0) and out-of-control average run length (ARL1) as statistical constraints. A Markov chain model is then developed to estimate the ARLs and a genetic algorithm whose parameters are optimally obtained by design of experiments is used to solve the model and estimate the optimum values of the control chart parameters. A numerical example and a sensitivity analysis are provided to illustrate the solution procedure and to investigate the effects of cost parameters on the optimal designs. The results show that the proposed economic-statistical design of the chart has better statistical properties in comparison to the economic design while the difference between the costs is negligible.
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