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A variables-type multiple-dependent-state sampling plan based on the lifetime performance index under a Weibull distribution

Author

Listed:
  • To-Cheng Wang

    (National Kaohsiung University of Science and Technology
    Republic of China Air Force Academy)

  • Chien-Wei Wu

    (National Tsing Hua University)

  • Ming-Hung Shu

    (National Kaohsiung University of Science and Technology
    Kaohsiung Medical University)

Abstract

Verification and validation of product quality highly affects the success of product design and manufacturing processes as well as the long-term collaboration between buyers and suppliers. Lifetime testing for acceptance is the main bottleneck in verification and validation and often requires considerable time and expense, especially in the current high-product-yield era. To establish a cost-effective and risk-controllable life testing sampling scheme, we propose a variables-type multiple-dependent-state (MDS) sampling plan using the lifetime performance index under a Weibull distribution with Type-II right censoring. The design parameters of the lifetime-capable MDS sampling plan are formulated as optimization models to minimize the required number of testing failures subject to nonlinear constraints for the desired lifetime capability levels and allowable risks regulated by the supplier and buyer. Compared with variable single sampling plans, the plan proposed herein is more cost-effective sampling and has greater discriminatory power. The applicability of the proposed plan is demonstrated through an example.

Suggested Citation

  • To-Cheng Wang & Chien-Wei Wu & Ming-Hung Shu, 2022. "A variables-type multiple-dependent-state sampling plan based on the lifetime performance index under a Weibull distribution," Annals of Operations Research, Springer, vol. 311(1), pages 381-399, April.
    • Handle: RePEc:spr:annopr:v:311:y:2022:i:1:d:10.1007_s10479-020-03655-z
    DOI: 10.1007/s10479-020-03655-z
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    References listed on IDEAS

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    1. Amy Lee & Chien-Wei Wu & Yen-Wen Chen, 2016. "A modified variables repetitive group sampling plan with the consideration of preceding lots information," Annals of Operations Research, Springer, vol. 238(1), pages 355-373, March.
    2. Yao Zhang & William Q. Meeker, 2005. "Bayesian life test planning for the Weibull distribution with given shape parameter," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 61(3), pages 237-249, June.
    3. Amy H. I. Lee & Chien-Wei Wu & Yen-Wen Chen, 2016. "A modified variables repetitive group sampling plan with the consideration of preceding lots information," Annals of Operations Research, Springer, vol. 238(1), pages 355-373, March.
    4. Balamurali, S. & Jun, Chi-Hyuck, 2007. "Multiple dependent state sampling plans for lot acceptance based on measurement data," European Journal of Operational Research, Elsevier, vol. 180(3), pages 1221-1230, August.
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    Cited by:

    1. Muhammad Aslam & Gadde Srinivasa Rao & Mohammed Albassam, 2022. "Sampling Inspection Plan to Test Daily COVID-19 Cases Using Gamma Distribution under Indeterminacy Based on Multiple Dependent Scheme," IJERPH, MDPI, vol. 19(9), pages 1-14, April.
    2. Sun, Chenhao & Xu, Hao & Zeng, Xiangjun & Wang, Wen & Jiang, Fei & Yang, Xin, 2023. "A vulnerability spatiotemporal distribution prognosis framework for integrated energy systems within intricate data scenes according to importance-fuzzy high-utility pattern identification," Applied Energy, Elsevier, vol. 344(C).
    3. Kuo-Ching Chiou & Tsun-Hung Huang & Kuen-Suan Chen & Chun-Min Yu, 2024. "Fuzzy Evaluation Model for Lifetime Performance Using Type-I Censoring Data," Mathematics, MDPI, vol. 12(13), pages 1-16, June.

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