IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0276908.html
   My bibliography  Save this article

System resilience distribution identification and analysis based on performance processes after disruptions

Author

Listed:
  • Yeqing Song
  • Ruiying Li

Abstract

Resilience is a system’s ability to withstand a disruption and return to a normal state quickly. It is a random variable due to the randomness of both the disruption and resilience behavior of a system. The distribution characteristics of resilience are the basis for resilience design and analysis, such as test sample size determination and assessment model selection. In this paper, we propose a systematic resilience distribution identification and analysis (RDIA) method based on a system’s performance processes after disruptions. Typical performance degradation/recovery processes have linear, exponential, and trigonometric functions, and they have three key parameters: the maximum performance degradation, the degradation duration, and the recovery duration. Using the Monte Carlo method, these three key parameters are first sampled according to their corresponding probability density functions. Combining the sample results with the given performance function type, the system performance curves after disruptions can be obtained. Then the sample resilience is computed using a deterministic resilience measure and the resilience distribution can be determined through candidate distribution identification, parameter estimation, and a goodness-of-fit test. Finally, we apply our RDIA method to systems with typical performance processes, and both the orthogonal experiment method and the control variable method are used to investigate the resilience distribution laws. The results show that the resilience of these systems follows the Weibull distribution. An end-to-end communication system is also used to explain how to apply this method with simulation or test data in practice.

Suggested Citation

  • Yeqing Song & Ruiying Li, 2022. "System resilience distribution identification and analysis based on performance processes after disruptions," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-21, November.
    • Handle: RePEc:plo:pone00:0276908
    DOI: 10.1371/journal.pone.0276908
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0276908
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0276908&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0276908?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ruiying Li & Qiang Dong & Chong Jin & Rui Kang, 2017. "A New Resilience Measure for Supply Chain Networks," Sustainability, MDPI, vol. 9(1), pages 1-19, January.
    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. Trucco, Paolo & Petrenj, Boris, 2023. "Characterisation of resilience metrics in full-scale applications to interdependent infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Pawlicka Kinga & Bal Monika, 2022. "Sustainable Supply Chain Finances implementation model and Artificial Intelligence for innovative omnichannel logistics," Management, Sciendo, vol. 26(1), pages 19-35, January.
    3. Zhaoming Yang & Qi Xiang & Yuxuan He & Shiliang Peng & Michael Havbro Faber & Enrico Zio & Lili Zuo & Huai Su & Jinjun Zhang, 2023. "Resilience of Natural Gas Pipeline System: A Review and Outlook," Energies, MDPI, vol. 16(17), pages 1-19, August.
    4. Lorenzo Bruno Prataviera & Alessandro Creazza & Marco Melacini & Fabrizio Dallari, 2022. "Heading for Tomorrow: Resilience Strategies for Post-COVID-19 Grocery Supply Chains," Sustainability, MDPI, vol. 14(4), pages 1-17, February.
    5. Maureen S. Golan & Laura H. Jernegan & Igor Linkov, 2020. "Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic," Environment Systems and Decisions, Springer, vol. 40(2), pages 222-243, June.
    6. Jialu Shi & Xuan Wang & Chengxin Wang & Haimeng Liu & Yi Miao & Fuyi Ci, 2022. "Evaluation and Influencing Factors of Network Resilience in Guangdong-Hong Kong-Macao Greater Bay Area: A Structural Perspective," Sustainability, MDPI, vol. 14(13), pages 1-21, June.
    7. Cornelis, Erwin, 2019. "History and prospect of voluntary agreements on industrial energy efficiency in Europe," Energy Policy, Elsevier, vol. 132(C), pages 567-582.
    8. Ruiying Li & Xiaoyu Tian & Li Yu & Rui Kang, 2019. "A Systematic Disturbance Analysis Method for Resilience Evaluation: A Case Study in Material Handling Systems," Sustainability, MDPI, vol. 11(5), pages 1-18, March.
    9. Ruiying Li & Qiang Dong & Wenting Ma & Rui Kang, 2023. "A test-based methodology for the probabilistic assessment of system resilience under random disturbances," Journal of Risk and Reliability, , vol. 237(4), pages 671-685, August.
    10. Khairy Sayed & Ahmed G. Abo-Khalil & Ali S. Alghamdi, 2019. "Optimum Resilient Operation and Control DC Microgrid Based Electric Vehicles Charging Station Powered by Renewable Energy Sources," Energies, MDPI, vol. 12(22), pages 1-23, November.
    11. Ding, Yueting & Zhang, Ming & Chen, Sai & Nie, Rui, 2020. "Assessing the resilience of China’s natural gas importation under network disruptions," Energy, Elsevier, vol. 211(C).
    12. Adel Mottahedi & Farhang Sereshki & Mohammad Ataei & Ali Nouri Qarahasanlou & Abbas Barabadi, 2021. "The Resilience of Critical Infrastructure Systems: A Systematic Literature Review," Energies, MDPI, vol. 14(6), pages 1-32, March.
    13. Ahmadian, Navid & Lim, Gino J. & Cho, Jaeyoung & Bora, Selim, 2020. "A quantitative approach for assessment and improvement of network resilience," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    14. Gao, Shengling & Li, Daqing & Zheng, Nan & Hu, Ruiqi & She, Zhikun, 2022. "Resilient perimeter control for hyper-congested two-region networks with MFD dynamics," Transportation Research Part B: Methodological, Elsevier, vol. 156(C), pages 50-75.
    15. Elena Candelo & Cecilia Casalegno & Chiara Civera & Fabrizio Mosca, 2018. "Turning Farmers into Business Partners through Value Co-Creation Projects. Insights from the Coffee Supply Chain," Sustainability, MDPI, vol. 10(4), pages 1-21, March.
    16. Kulkarni, Onkar & Dahan, Mathieu & Montreuil, Benoit, 2022. "Resilient Hyperconnected Parcel Delivery Network Design Under Disruption Risks," International Journal of Production Economics, Elsevier, vol. 251(C).
    17. D. G. Mogale & Xun Wang & Emrah Demir & Vasco Sanchez Rodrigues, 2023. "Modelling and analysing supply chain disruption: a case of online grocery retailer," Operations Management Research, Springer, vol. 16(4), pages 1901-1924, December.
    18. Reza Kiani Mavi & Neda Kiani Mavi & Seyed Ashkan Hosseini Shekarabi & Matthew Pepper’s & Sean Arisian, 2023. "Supply Chain Resilience: A Common Weights Efficiency Analysis with Non-discretionary and Non-controllable Inputs," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(1), pages 77-99, December.
    19. Jiguang Wang & Yucai Wu, 2018. "An Improved Voronoi-Diagram-Based Algorithm for Continuous Facility Location Problem under Disruptions," Sustainability, MDPI, vol. 10(9), pages 1-13, August.
    20. Zeplin Jiwa Husada Tarigan & Hotlan Siagian & Ferry Jie, 2021. "Impact of Internal Integration, Supply Chain Partnership, Supply Chain Agility, and Supply Chain Resilience on Sustainable Advantage," Sustainability, MDPI, vol. 13(10), pages 1-18, May.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0276908. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.