IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i5p1100-d1077179.html
   My bibliography  Save this article

On Importance of Sensitivity Analysis on an Example of a k -out-of- n System

Author

Listed:
  • Nika Ivanova

    (Department of Applied Probability and Informatics, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Str., 117198 Moscow, Russia
    V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, 65 Profsoyuznaya Str., 117997 Moscow, Russia)

Abstract

Reliability and sensitivity issues are very close and important problems in any technical system. The system’s sensitivity is understood as the dependence of its behavior on changes in some internal parameters. To perform sensitivity analysis, a general procedure based on a theoretical and numerical study is proposed and applied to a repairable k -out-of- n model. The results show the asymptotic insensitivity of the non-stationary and stationary characteristics of the system reliability to the shape of the repair-time distribution, as well as to the value of its coefficient of variation at a fixed mean. The proposed methodology can be useful to researchers and engineers at the designing stage of real systems, as well as applied to other stochastic reliability models.

Suggested Citation

  • Nika Ivanova, 2023. "On Importance of Sensitivity Analysis on an Example of a k -out-of- n System," Mathematics, MDPI, vol. 11(5), pages 1-18, February.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1100-:d:1077179
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/5/1100/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/5/1100/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dmitry Efrosinin & Natalia Stepanova & Janos Sztrik & Andreas Plank, 2020. "Approximations in Performance Analysis of a Controllable Queueing System with Heterogeneous Servers," Mathematics, MDPI, vol. 8(10), pages 1-18, October.
    2. Hector Gibson Kinmanhon Houankpo & Dmitry Kozyrev, 2021. "Mathematical and Simulation Model for Reliability Analysis of a Heterogeneous Redundant Data Transmission System," Mathematics, MDPI, vol. 9(22), pages 1-16, November.
    3. Vladimir Rykov & Nika Ivanova & Dmitry Kozyrev, 2021. "Application of Decomposable Semi-Regenerative Processes to the Study of k -out-of- n Systems," Mathematics, MDPI, vol. 9(16), pages 1-23, August.
    4. Evsey Morozov & Michele Pagano & Irina Peshkova & Alexander Rumyantsev, 2020. "Sensitivity Analysis and Simulation of a Multiserver Queueing System with Mixed Service Time Distribution," Mathematics, MDPI, vol. 8(8), pages 1-16, August.
    5. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2021. "Sustainable Supply Chains with Blockchain, IoT and RFID: A Simulation on Order Management," Sustainability, MDPI, vol. 13(11), pages 1-23, June.
    2. Makam, Vaishno Devi & Millossovich, Pietro & Tsanakas, Andreas, 2021. "Sensitivity analysis with χ2-divergences," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 372-383.
    3. Plischke, Elmar & Borgonovo, Emanuele, 2019. "Copula theory and probabilistic sensitivity analysis: Is there a connection?," European Journal of Operational Research, Elsevier, vol. 277(3), pages 1046-1059.
    4. Wen Shi & Xi Chen & Jennifer Shang, 2019. "An Efficient Morris Method-Based Framework for Simulation Factor Screening," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 745-770, October.
    5. F. Wang & G. H. Huang & Y. Fan & Y. P. Li, 2020. "Robust Subsampling ANOVA Methods for Sensitivity Analysis of Water Resource and Environmental Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3199-3217, August.
    6. Daniel Harenberg & Stefano Marelli & Bruno Sudret & Viktor Winschel, 2019. "Uncertainty quantification and global sensitivity analysis for economic models," Quantitative Economics, Econometric Society, vol. 10(1), pages 1-41, January.
    7. Tobias Fissler & Silvana M. Pesenti, 2022. "Sensitivity Measures Based on Scoring Functions," Papers 2203.00460, arXiv.org, revised Jul 2022.
    8. Shang, Xiaobing & Su, Li & Fang, Hai & Zeng, Bowen & Zhang, Zhi, 2023. "An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    9. Magni, Carlo Alberto, 2016. "Capital depreciation and the underdetermination of rate of return: A unifying perspective," Journal of Mathematical Economics, Elsevier, vol. 67(C), pages 54-79.
    10. Lu, Xuefei & Borgonovo, Emanuele, 2023. "Global sensitivity analysis in epidemiological modeling," European Journal of Operational Research, Elsevier, vol. 304(1), pages 9-24.
    11. Matteo Fontana & Massimo Tavoni & Simone Vantini, 2020. "Global Sensitivity and Domain-Selective Testing for Functional-Valued Responses: An Application to Climate Economy Models," Papers 2006.13850, arXiv.org, revised Apr 2024.
    12. Stefano Cucurachi & Carlos Felipe Blanco & Bernhard Steubing & Reinout Heijungs, 2022. "Implementation of uncertainty analysis and moment‐independent global sensitivity analysis for full‐scale life cycle assessment models," Journal of Industrial Ecology, Yale University, vol. 26(2), pages 374-391, April.
    13. Yun, Wanying & Lu, Zhenzhou & Feng, Kaixuan & Li, Luyi, 2019. "An elaborate algorithm for analyzing the Borgonovo moment-independent sensitivity by replacing the probability density function estimation with the probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 99-108.
    14. Puppo, L. & Pedroni, N. & Maio, F. Di & Bersano, A. & Bertani, C. & Zio, E., 2021. "A Framework based on Finite Mixture Models and Adaptive Kriging for Characterizing Non-Smooth and Multimodal Failure Regions in a Nuclear Passive Safety System," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    15. Ziemele, Jelena & Gravelsins, Armands & Blumberga, Andra & Blumberga, Dagnija, 2017. "Sustainability of heat energy tariff in district heating system: Statistic and dynamic methodologies," Energy, Elsevier, vol. 137(C), pages 834-845.
    16. Thomas H. Jørgensen, 2023. "Sensitivity to Calibrated Parameters," The Review of Economics and Statistics, MIT Press, vol. 105(2), pages 474-481, March.
    17. Marchioni, Andrea & Magni, Carlo Alberto, 2018. "Investment decisions and sensitivity analysis: NPV-consistency of rates of return," European Journal of Operational Research, Elsevier, vol. 268(1), pages 361-372.
    18. Magni, Carlo Alberto & Marchioni, Andrea, 2020. "Average rates of return, working capital, and NPV-consistency in project appraisal: A sensitivity analysis approach," International Journal of Production Economics, Elsevier, vol. 229(C).
    19. Aigner, Philipp & Schlütter, Sebastian, 2023. "Enhancing gradient capital allocation with orthogonal convexity scenarios," ICIR Working Paper Series 47/23, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR).
    20. Pesenti, Silvana M. & Millossovich, Pietro & Tsanakas, Andreas, 2019. "Reverse sensitivity testing: What does it take to break the model?," European Journal of Operational Research, Elsevier, vol. 274(2), pages 654-670.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1100-:d:1077179. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.