Economic and Economic-Statistical Designs of MEWMA Control Charts-A Hybrid Taguchi Loss, Markov Chain and Genetic Algorithm Approach
نویسندگان
چکیده
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 [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
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