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Variable parameter and double sampling charts in the presence of correlation: The Markov chain approach

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  • Costa, Antonio Fernando Branco
  • Machado, Marcela Aparecida Guerreiro

Abstract

The general assumption under which the chart is designed is that the process mean has a constant in-control value. However, there are situations in which the process mean wanders. When it wanders according to a first-order autoregressive (AR (1)) model, a complex approach involving Markov chains and integral equation methods is used to evaluate the properties of the chart. In this paper, we propose the use of a pure Markov chain approach to study the performance of the chart. The performance of the chat with variable parameters and the with double sampling are compared.

Suggested Citation

  • Costa, Antonio Fernando Branco & Machado, Marcela Aparecida Guerreiro, 2011. "Variable parameter and double sampling charts in the presence of correlation: The Markov chain approach," International Journal of Production Economics, Elsevier, vol. 130(2), pages 224-229, April.
    • Handle: RePEc:eee:proeco:v:130:y:2011:i:2:p:224-229
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    References listed on IDEAS

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    1. Machado, Marcela A.G. & Costa, Antonio F.B., 2008. "The double sampling and the EWMA charts based on the sample variances," International Journal of Production Economics, Elsevier, vol. 114(1), pages 134-148, July.
    2. De Magalhães, M.S. & Costa, A.F.B. & Moura Neto, F.D., 2009. "A hierarchy of adaptive control charts," International Journal of Production Economics, Elsevier, vol. 119(2), pages 271-283, June.
    3. 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.
    4. Lin, Yu-Chang & Chou, Chao-Yu, 2005. "On the design of variable sample size and sampling intervals charts under non-normality," International Journal of Production Economics, Elsevier, vol. 96(2), pages 249-261, May.
    5. Bai, D. S. & Lee, K. T., 2002. "Variable sampling interval control charts with an improved switching rule," International Journal of Production Economics, Elsevier, vol. 76(2), pages 189-199, March.
    6. Torng, Chau-Chen & Lee, Pei-Hsi & Liao, Nai-Yi, 2009. "An economic-statistical design of double sampling control chart," International Journal of Production Economics, Elsevier, vol. 120(2), pages 495-500, August.
    7. Villalobos, J. Rene & Munoz, Luis & Gutierrez, Marco A., 2005. "Using fixed and adaptive multivariate SPC charts for online SMD assembly monitoring," International Journal of Production Economics, Elsevier, vol. 95(1), pages 109-121, January.
    8. Liu, J.Y. & Xie, M. & Goh, T.N. & Liu, Q.H. & Yang, Z.H., 2006. "Cumulative count of conforming chart with variable sampling intervals," International Journal of Production Economics, Elsevier, vol. 101(2), pages 286-297, June.
    9. De Magalhaes, Maysa S. & B. Costa, Antonio F. & Moura Neto, Francisco D., 2006. "Adaptive control charts: A Markovian approach for processes subject to independent disturbances," International Journal of Production Economics, Elsevier, vol. 99(1-2), pages 236-246, February.
    10. Zhang Wu & Jianxin Jiao & Mei Yang & Ying Liu & Zhaojun Wang, 2009. "An enhanced adaptive CUSUM control chart," IISE Transactions, Taylor & Francis Journals, vol. 41(7), pages 642-653.
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    Cited by:

    1. Du, Shichang & Lv, Jun, 2013. "Minimal Euclidean distance chart based on support vector regression for monitoring mean shifts of auto-correlated processes," International Journal of Production Economics, Elsevier, vol. 141(1), pages 377-387.
    2. Franco, Bruno Chaves & Celano, Giovanni & Castagliola, Philippe & Costa, Antonio Fernando Branco, 2014. "Economic design of Shewhart control charts for monitoring autocorrelated data with skip sampling strategies," International Journal of Production Economics, Elsevier, vol. 151(C), pages 121-130.
    3. Ou, Yanjing & Wu, Zhang & Tsung, Fugee, 2012. "A comparison study of effectiveness and robustness of control charts for monitoring process mean," International Journal of Production Economics, Elsevier, vol. 135(1), pages 479-490.
    4. Iziy Azamsadat & Sadeghpour Gildeh Bahram & Monabbati Ehsan, 2017. "Comparison Between the Economic-Statistical Design of Double and Triple Sampling X¯\bar{X} Control Charts," Stochastics and Quality Control, De Gruyter, vol. 32(1), pages 49-61, June.
    5. Lim, S.L. & Khoo, Michael B.C. & Teoh, W.L. & Xie, M., 2015. "Optimal designs of the variable sample size and sampling interval X¯ chart when process parameters are estimated," International Journal of Production Economics, Elsevier, vol. 166(C), pages 20-35.
    6. Zhang, Min & Nie, Guohua & He, Zhen, 2014. "Performance of cumulative count of conforming chart of variable sampling intervals with estimated control limits," International Journal of Production Economics, Elsevier, vol. 150(C), pages 114-124.
    7. Khoo, Michael B.C. & Teoh, W.L. & Castagliola, Philippe & Lee, M.H., 2013. "Optimal designs of the double sampling X¯ chart with estimated parameters," International Journal of Production Economics, Elsevier, vol. 144(1), pages 345-357.
    8. Leoni, Roberto Campos & Costa, Antonio Fernando Branco & Machado, Marcela Aparecida Guerreiro, 2015. "The effect of the autocorrelation on the performance of the T2 chart," European Journal of Operational Research, Elsevier, vol. 247(1), pages 155-165.
    9. Ahmad, Shabbir & Riaz, Muhammad & Abbasi, Saddam Akber & Lin, Zhengyan, 2013. "On monitoring process variability under double sampling scheme," International Journal of Production Economics, Elsevier, vol. 142(2), pages 388-400.
    10. Roberto Campos Leoni & Marcela Aparecida Guerreiro Machado & Antonio Fernando Branco Costa, 2016. "The T -super-2 chart with mixed samples to control bivariate autocorrelated processes," International Journal of Production Research, Taylor & Francis Journals, vol. 54(11), pages 3294-3310, June.

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