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Production variability in manufacturing systems: Bernoulli reliability case

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  • Jingshan Li
  • Semyon Meerkov

Abstract

The problem of production variability in serial manufacturing lines with unreliable machines is addressed. Bernoulli statistics of machine reliability are assumed. Three problems are considered: the problem of production variance, the problem of constant demand satisfaction, and the problem of random demand satisfaction generated by another (unreliable) production line. For all three problems, bounds on the respective variability measures are derived. These bounds show that long lines smooth out the production and reduce the variability. More precisely, these bounds state that the production variability of a line with many machines is smaller than that of a single machine system with production volume and reliability characteristics similar to those of the longer line. Since all the variability measures for a single machine line can be calculated relatively easily, these bounds provide analytical tools for analysis and design of serial production lines from the point of view of the customer demand satisfaction. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • Jingshan Li & Semyon Meerkov, 2000. "Production variability in manufacturing systems: Bernoulli reliability case," Annals of Operations Research, Springer, vol. 93(1), pages 299-324, January.
    • Handle: RePEc:spr:annopr:v:93:y:2000:i:1:p:299-324:10.1023/a:1018928007956
    DOI: 10.1023/A:1018928007956
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    Cited by:

    1. Kucuksayacigil, Fikri & Roni, Mohammad & Eksioglu, Sandra D. & Bhuiyan, Tanveer H. & Chen, Qiushi, 2022. "Optimal control to handle variations in moisture content and reactor in-feed rate," Energy, Elsevier, vol. 248(C).
    2. Peiqi Yang & Zhi Pei, 2022. "Energy-Saving Manufacturing System Design with Two Geometric Machines," Sustainability, MDPI, vol. 14(18), pages 1-21, September.
    3. Xiang Zhong & Jie Song & Jingshan Li & Susan M. Ertl & Lauren Fiedler, 2016. "Design and analysis of gastroenterology (GI) clinic in Digestive Health Center of University of Wisconsin Health," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 90-119, June.
    4. Dauzère-Pérès, Stéphane & Hassoun, Michael, 2020. "On the importance of variability when managing metrology capacity," European Journal of Operational Research, Elsevier, vol. 282(1), pages 267-276.
    5. George Liberopoulos, 2020. "Comparison of optimal buffer allocation in flow lines under installation buffer, echelon buffer, and CONWIP policies," Flexible Services and Manufacturing Journal, Springer, vol. 32(2), pages 297-365, June.

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