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Economic-statistical design of simple linear profiles with variable sampling interval

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  • M.J. Ershadi
  • R. Noorossana
  • S.T.A Niaki

Abstract

Control charts are statistical tools to monitor a process or a product. However, some processes cannot be controlled by monitoring a characteristic; instead, they need to be monitored using profiles. Economic-statistical design of profile monitoring means determining the parameters of a profile monitoring scheme such that total costs are minimized while statistical measures maintain proper values. While varying sampling interval usually increases the effectiveness of profile monitoring, economic-statistical design of variable sampling interval (VSI) profile monitoring is investigated in this paper. An extended Lorenzen--Vance function is used for modeling total costs in VSI model where the average time to signal is employed for depicting the statistical measure of the obtained profile monitoring scheme. Two sampling intervals; number of set points and the parameters of control charts that are used in profile monitoring are the variables that are obtained thorough the economic-statistical model. A genetic algorithm is employed to optimize the model and an experimental design approach is used for tuning its parameters. Sensitivity analysis and numerical results indicate satisfactory performance for the proposed model.

Suggested Citation

  • M.J. Ershadi & R. Noorossana & S.T.A Niaki, 2016. "Economic-statistical design of simple linear profiles with variable sampling interval," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(8), pages 1400-1418, June.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:8:p:1400-1418
    DOI: 10.1080/02664763.2015.1103705
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    References listed on IDEAS

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    1. Serel, Dogan A. & Moskowitz, Herbert, 2008. "Joint economic design of EWMA control charts for mean and variance," European Journal of Operational Research, Elsevier, vol. 184(1), pages 157-168, January.
    2. Mahmoud Mahmoud & J. P. Morgan & William Woodall, 2010. "The monitoring of simple linear regression profiles with two observations per sample," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(8), pages 1249-1263.
    3. Seyed Niaki & Mohammad Ershadi, 2012. "A parameter-tuned genetic algorithm for statistically constrained economic design of multivariate CUSUM control charts: a Taguchi loss approach," International Journal of Systems Science, Taylor & Francis Journals, vol. 43(12), pages 2275-2287.
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    Cited by:

    1. Amir Ahmadi-Javid & Mohsen Ebadi, 2017. "Economic Design of Memory-Type Control Charts: The Fallacy of the Formula Proposed by Lorenzen and Vance (1986)," Papers 1708.06160, arXiv.org.
    2. Amir Ahmadi-Javid & Mohsen Ebadi, 2021. "Economic design of memory-type control charts: The fallacy of the formula proposed by Lorenzen and Vance (1986)," Computational Statistics, Springer, vol. 36(1), pages 661-690, March.

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