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Maximization of Manufacturing Yield of Systems with Arbitrary Distributions of Component Values

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  • Abbas Seifi
  • K. Ponnambalam
  • Jiri Vlach

Abstract

This paper presents a general method for maximizing manufacturing yield when the realizations of system components are independent random variables with arbitrary distributions. Design specifications define a feasible region which, in the nonlinear case, is linearized using a first-order approximation. The method attempts to place the given tolerance hypercube of the uncertain parameters such that the area with higher yield lies in the feasible region. The yield is estimated by using the joint cumulative density function over the portion of the tolerance hypercube that is contained in the feasible region. A double-bounded density function is used to approximate various bounded distributions for which optimal designs are demonstrated on a tutorial example. Monte Carlo simulation is used to evaluate the actual yields of optimal designs. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • Abbas Seifi & K. Ponnambalam & Jiri Vlach, 2000. "Maximization of Manufacturing Yield of Systems with Arbitrary Distributions of Component Values," Annals of Operations Research, Springer, vol. 99(1), pages 373-383, December.
    • Handle: RePEc:spr:annopr:v:99:y:2000:i:1:p:373-383:10.1023/a:1019288220413
    DOI: 10.1023/A:1019288220413
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    Citations

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    Cited by:

    1. Pablo Mitnik & Sunyoung Baek, 2013. "The Kumaraswamy distribution: median-dispersion re-parameterizations for regression modeling and simulation-based estimation," Statistical Papers, Springer, vol. 54(1), pages 177-192, February.
    2. Vlad Stefan Barbu & Alex Karagrigoriou & Andreas Makrides, 2021. "Reliability and Inference for Multi State Systems: The Generalized Kumaraswamy Case," Mathematics, MDPI, vol. 9(16), pages 1-17, August.
    3. Amarjit Kundu & Shovan Chowdhury, 2019. "Ordering properties of the largest order statistics from Kumaraswamy-G models under random shocks," Working papers 297, Indian Institute of Management Kozhikode.
    4. Khan Muhammad Shuaib & King Robert & Hudson Irene Lena, 2016. "Transmuted Kumaraswamy Distribution," Statistics in Transition New Series, Polish Statistical Association, vol. 17(2), pages 183-210, June.
    5. Mustafa Nadar & Fatih Kızılaslan, 2014. "Classical and Bayesian estimation of $$P(X>Y)$$ P ( X > Y ) using upper record values from Kumaraswamy’s distribution," Statistical Papers, Springer, vol. 55(3), pages 751-783, August.
    6. Gauss Cordeiro & Saralees Nadarajah & Edwin Ortega, 2012. "The Kumaraswamy Gumbel distribution," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(2), pages 139-168, June.
    7. Muhammad Shuaib Khan & Robert King & Irene Lena Hudson, 2016. "Transmuted Kumaraswamy Distribution," Statistics in Transition New Series, Polish Statistical Association, vol. 17(2), pages 183-210, June.
    8. Robert King & Irene Lena Hudson & Muhammad Shuaib Khan, 2016. "Transmuted Kumaraswamy Distribution," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 17(2), pages 183-210, June.
    9. Mustafa Nadar & Alexander Papadopoulos & Fatih Kızılaslan, 2013. "Statistical analysis for Kumaraswamy’s distribution based on record data," Statistical Papers, Springer, vol. 54(2), pages 355-369, May.
    10. Farha Sultana & Yogesh Mani Tripathi & Shuo-Jye Wu & Tanmay Sen, 2022. "Inference for Kumaraswamy Distribution Based on Type I Progressive Hybrid Censoring," Annals of Data Science, Springer, vol. 9(6), pages 1283-1307, December.

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