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Burn-in and the performance quality measures in heterogeneous populations

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  • Cha, Ji Hwan
  • Finkelstein, Maxim

Abstract

Burn-in is a widely used engineering method of elimination of defective items before they are shipped to customers or put into field operation. Under the assumption that a population is described by the decreasing or bathtub-shaped failure rate functions, various optimal burn-in problems have been intensively studied in the literature. In this paper, we consider a new model and assume that a population is composed of stochastically ordered subpopulations described by their own performance quality measures. It turns out that this setting can justify burn-in even in situations when it is not justified in the framework of conventional approaches. For instance, it is shown that it can be reasonable to perform burn-in even when the failure rate function that describes the heterogeneous population of items increases and this is one of the main and important findings of our study.

Suggested Citation

  • Cha, Ji Hwan & Finkelstein, Maxim, 2011. "Burn-in and the performance quality measures in heterogeneous populations," European Journal of Operational Research, Elsevier, vol. 210(2), pages 273-280, April.
  • Handle: RePEc:eee:ejores:v:210:y:2011:i:2:p:273-280
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    References listed on IDEAS

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    1. Chang, Dong Shang, 2000. "Optimal burn-in decision for products with an unimodal failure rate function," European Journal of Operational Research, Elsevier, vol. 126(3), pages 534-540, November.
    2. Maxim Finkelstein, 2008. "Failure Rate Modelling for Reliability and Risk," Springer Series in Reliability Engineering, Springer, number 978-1-84800-986-8, March.
    3. Cha, Ji Hwan & Finkelstein, Maxim, 2010. "Burn-in by environmental shocks for two ordered subpopulations," European Journal of Operational Research, Elsevier, vol. 206(1), pages 111-117, October.
    4. Maxim Finkelstein, 2009. "Understanding the shape of the mixture failure rate (with engineering and demographic applications)," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(6), pages 643-663, November.
    5. Maxim S. Finkelstein, 2009. "Understanding the shape of the mixture failure rate (with engineering and demographic applications)," MPIDR Working Papers WP-2009-031, Max Planck Institute for Demographic Research, Rostock, Germany.
    6. Colangelo, Antonio & Scarsini, Marco & Shaked, Moshe, 2006. "Some positive dependence stochastic orders," Journal of Multivariate Analysis, Elsevier, vol. 97(1), pages 46-78, January.
    7. Sheu, Shey-Huei & Chien, Yu-Hung, 2005. "Optimal burn-in time to minimize the cost for general repairable products sold under warranty," European Journal of Operational Research, Elsevier, vol. 163(2), pages 445-461, June.
    8. Jie Mi, 1996. "Minimizing Some Cost Functions Related to Both Burn-In and Field Use," Operations Research, INFORMS, vol. 44(3), pages 497-500, June.
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    Citations

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    Cited by:

    1. Ye, Zhi-Sheng & Shen, Yan & Xie, Min, 2012. "Degradation-based burn-in with preventive maintenance," European Journal of Operational Research, Elsevier, vol. 221(2), pages 360-367.
    2. Ji Hwan Cha & Maxim Finkelstein, 2012. "Burn-in and the performance quality measures in continuous heterogeneous populations," Journal of Risk and Reliability, , vol. 226(4), pages 417-425, August.
    3. Cha, Ji Hwan & Finkelstein, Maxim & Levitin, Gregory, 2018. "Optimal mission abort policy for partially repairable heterogeneous systems," European Journal of Operational Research, Elsevier, vol. 271(3), pages 818-825.
    4. Zhai, Qingqing & Ye, Zhi-Sheng & Yang, Jun & Zhao, Yu, 2016. "Measurement errors in degradation-based burn-in," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 126-135.
    5. Cha, Ji Hwan & Pulcini, Gianpaolo, 2016. "Optimal burn-in procedure for mixed populations based on the device degradation process history," European Journal of Operational Research, Elsevier, vol. 251(3), pages 988-998.
    6. Kim, Kyungmee O., 2011. "Burn-in considering yield loss and reliability gain for integrated circuits," European Journal of Operational Research, Elsevier, vol. 212(2), pages 337-344, July.
    7. Nil Kamal Hazra & Maxim Finkelstein, 2018. "On stochastic comparisons of finite mixtures for some semiparametric families of distributions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(4), pages 988-1006, December.
    8. Fernández, Arturo J., 2012. "Minimizing the area of a Pareto confidence region," European Journal of Operational Research, Elsevier, vol. 221(1), pages 205-212.
    9. Cha, Ji Hwan & Finkelstein, Maxim, 2013. "The failure rate dynamics in heterogeneous populations," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 120-128.
    10. Safaei, Fatemeh & Taghipour, Sharareh, 2024. "Integrated degradation-based burn-in and maintenance model for heterogeneous and highly reliable items," Reliability Engineering and System Safety, Elsevier, vol. 244(C).

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