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The optimization design of EWMA charts for monitoring environmental performance

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  • Yu-min Liu
  • Li Xue

Abstract

Aimed at reducing the loss in the process of controlling environmental pollution, this paper proposes an optimization design of EWMA charts for monitoring and evaluating the environmental performance. By using Rayleigh distribution to approximate probability distribution of the random process with mean shifts, the parameters of the EWMA charts for non-normal data will be determined such that the overall mean of Taguchi’s loss function (ML) is minimized. Based on these optimal parameters, ML-EWMA charts are constructed for monitoring environmental pollution processes. Then further analysis has been performed to compare the optimization of the ML-EWMA charts and the conventional EWMA charts in terms of their expected losses, the results of which show that ML-EWMA charts in this paper are significantly superior to the conventional EWMA charts as far as the overall loss of ML is concerned. Finally, a numeric example is illustrated to show the application of optimization design of EWMA charts for non-normal environmental data. The optimization design method proposed in this paper can reduce the loss greatly in environmental control and the general idea can be applied in other control charts. Copyright Springer Science+Business Media New York 2015

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  • Yu-min Liu & Li Xue, 2015. "The optimization design of EWMA charts for monitoring environmental performance," Annals of Operations Research, Springer, vol. 228(1), pages 113-124, May.
    • Handle: RePEc:spr:annopr:v:228:y:2015:i:1:p:113-124:10.1007/s10479-012-1239-6
    DOI: 10.1007/s10479-012-1239-6
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    1. Corbett, Charles J. & Pan, Jeh-Nan, 2002. "Evaluating environmental performance using statistical process control techniques," European Journal of Operational Research, Elsevier, vol. 139(1), pages 68-83, May.
    2. Chen, Yan-Kwang, 2004. "Economic design of control charts for non-normal data using variable sampling policy," International Journal of Production Economics, Elsevier, vol. 92(1), pages 61-74, November.
    3. Jing Hu & George Runger, 2010. "Time-based detection of changes to multivariate patterns," Annals of Operations Research, Springer, vol. 174(1), pages 67-81, February.
    4. Changliang Zou & Xianghui Ning & Fugee Tsung, 2012. "LASSO-based multivariate linear profile monitoring," Annals of Operations Research, Springer, vol. 192(1), pages 3-19, January.
    5. K.‐H. Waldmann, 1986. "Bounds for the Distribution of the Run Length of Geometric Moving Average Charts," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 35(2), pages 151-158, June.
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