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Measuring and optimizing system reliability: a stochastic programming approach

Author

Listed:
  • Joshua L. Pulsipher

    (University of Wisconsin-Madison)

  • Victor M. Zavala

    (University of Wisconsin-Madison)

Abstract

We propose a computational framework to quantify (measure) and to optimize the reliability of complex systems. The approach uses a graph representation of the system that is subject to random failures of its components (nodes and edges). Under this setting, reliability is defined as the probability of finding a path between sources and sink nodes under random component failures and we show that this measure can be computed by solving a stochastic mixed-integer program. The stochastic programming setting allows us to account for system constraints and general probability distributions to characterize failures and allows us to derive optimization formulations that identify designs of maximum reliability. We also propose a strategy to approximately solve these problems in a scalable manner by using purely continuous formulations.

Suggested Citation

  • Joshua L. Pulsipher & Victor M. Zavala, 2020. "Measuring and optimizing system reliability: a stochastic programming approach," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 626-645, October.
    • Handle: RePEc:spr:topjnl:v:28:y:2020:i:3:d:10.1007_s11750-020-00550-5
    DOI: 10.1007/s11750-020-00550-5
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    References listed on IDEAS

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    1. Kim, Youngsuk & Kang, Won-Hee, 2013. "Network reliability analysis of complex systems using a non-simulation-based method," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 80-88.
    2. Bistouni, Fathollah & Jahanshahi, Mohsen, 2014. "Analyzing the reliability of shuffle-exchange networks using reliability block diagrams," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 97-106.
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