IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v241y2024ics0951832023005343.html
   My bibliography  Save this article

A novel methodology to model disruption propagation for resilient maritime transportation systems–a case study of the Arctic maritime transportation system

Author

Listed:
  • Liu, Yang
  • Ma, Xiaoxue
  • Qiao, Weiliang
  • Ma, Laihao
  • Han, Bing

Abstract

Disruption cognition is critical for the development of resilient maritime transportation systems to withstand uncertain risks and achieve sustainable development. Aiming at improving the resilience of maritime transportation systems, a comprehensive methodology is proposed in the present study to model the propagation process of disruptions. First, a conceptual framework of disruption propagation within resilience theory is developed for the maritime transportation system, based on which a directed weighted complex network of disruption propagation is established, and data-driven Bayesian inference is applied to extend the complex network using a probability-based method. The propagation process and mechanisms can then be analyzed quantitatively through critical node identification for each propagation stage and the determination of the shortest propagative paths by the combination of bidirectional Bayesian inference, sensitivity analysis, and uncertainty analysis. Then, the proposed methodology is applied to the Arctic maritime transportation system to improve resilience by controlling the key disruptions in each propagation stage and cutting off the critical disruption propagation paths. The findings suggest that greater effort should be devoted to strengthening the resilience aspects related to environmental forecast and route planning systems, monitoring and functional maintenance mechanisms, emergency responses pertaining to repair and damage control, emergency escape and evacuation, and coastal SAR services to reduce the escalated impact of disruption propagation.

Suggested Citation

  • Liu, Yang & Ma, Xiaoxue & Qiao, Weiliang & Ma, Laihao & Han, Bing, 2024. "A novel methodology to model disruption propagation for resilient maritime transportation systems–a case study of the Arctic maritime transportation system," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:reensy:v:241:y:2024:i:c:s0951832023005343
    DOI: 10.1016/j.ress.2023.109620
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832023005343
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2023.109620?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fu, Shanshan & Zhang, Di & Montewka, Jakub & Yan, Xinping & Zio, Enrico, 2016. "Towards a probabilistic model for predicting ship besetting in ice in Arctic waters," Reliability Engineering and System Safety, Elsevier, vol. 155(C), pages 124-136.
    2. Ma, Xiaoxue & Deng, Wanyi & Qiao, Weiliang & Lan, He, 2022. "A methodology to quantify the risk propagation of hazardous events for ship grounding accidents based on directed CN," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    3. Theo Notteboom & Thanos Pallis & Jean-Paul Rodrigue, 2021. "Disruptions and resilience in global container shipping and ports: the COVID-19 pandemic versus the 2008–2009 financial crisis," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(2), pages 179-210, June.
    4. Seyedmohsen Hosseini & Dmitry Ivanov, 2022. "A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach," Annals of Operations Research, Springer, vol. 319(1), pages 581-607, December.
    5. Ash, J. & Newth, D., 2007. "Optimizing complex networks for resilience against cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 673-683.
    6. Ritesh Ojha & Abhijeet Ghadge & Manoj Kumar Tiwari & Umit S. Bititci, 2018. "Bayesian network modelling for supply chain risk propagation," International Journal of Production Research, Taylor & Francis Journals, vol. 56(17), pages 5795-5819, September.
    7. Fan, Dongming & Sun, Bo & Dui, Hongyan & Zhong, Jilong & Wang, Ziyao & Ren, Yi & Wang, Zili, 2022. "A modified connectivity link addition strategy to improve the resilience of multiplex networks against attacks," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    8. Berle, Øyvind & Asbjørnslett, Bjørn Egil & Rice, James B., 2011. "Formal Vulnerability Assessment of a maritime transportation system," Reliability Engineering and System Safety, Elsevier, vol. 96(6), pages 696-705.
    9. Shanshan Fu & Xinping Yan & Di Zhang & Minyang Zhang, 2018. "Risk influencing factors analysis of Arctic maritime transportation systems: a Chinese perspective," Maritime Policy & Management, Taylor & Francis Journals, vol. 45(4), pages 439-455, May.
    10. Jin, Ziyang & Duan, Dongli & Wang, Ning, 2022. "Cascading failure of complex networks based on load redistribution and epidemic process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    11. Øyvind Berle & James B. Rice Jr. & Bjørn Egil Asbørnslett, 2011. "Failure modes in the maritime transportation system: a functional approach to throughput vulnerability," Maritime Policy & Management, Taylor & Francis Journals, vol. 38(6), pages 605-632, March.
    12. Lan, He & Ma, Xiaoxue & Qiao, Weiliang & Ma, Laihao, 2022. "On the causation of seafarers’ unsafe acts using grounded theory and association rule," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    13. Yiping Fang & Nicola Pedroni & Enrico Zio, 2015. "Optimization of Cascade‐Resilient Electrical Infrastructures and its Validation by Power Flow Modeling," Risk Analysis, John Wiley & Sons, vol. 35(4), pages 594-607, April.
    14. Chen, Chao & Yang, Ming & Reniers, Genserik, 2021. "A dynamic stochastic methodology for quantifying HAZMAT storage resilience," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    15. Abedi, Amin & Gaudard, Ludovic & Romerio, Franco, 2019. "Review of major approaches to analyze vulnerability in power system," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 153-172.
    16. Li, Yuhong & Chen, Kedong & Collignon, Stephane & Ivanov, Dmitry, 2021. "Ripple effect in the supply chain network: Forward and backward disruption propagation, network health and firm vulnerability," European Journal of Operational Research, Elsevier, vol. 291(3), pages 1117-1131.
    17. Woods, David D., 2015. "Four concepts for resilience and the implications for the future of resilience engineering," Reliability Engineering and System Safety, Elsevier, vol. 141(C), pages 5-9.
    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. Yao He & Yongchun Yang & Meimei Wang & Xudong Zhang, 2022. "Resilience Analysis of Container Port Shipping Network Structure: The Case of China," Sustainability, MDPI, vol. 14(15), pages 1-17, August.
    2. Xueni Gou & Jasmine Siu Lee Lam, 2019. "Risk analysis of marine cargoes and major port disruptions," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 21(4), pages 497-523, December.
    3. Zou, Qiling & Chen, Suren, 2019. "Enhancing resilience of interdependent traffic-electric power system," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    4. Watson, Bryan C & Morris, Zack B & Weissburg, Marc & Bras, Bert, 2023. "System of system design-for-resilience heuristics derived from forestry case study variants," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    5. Cerqueti, Roy & Ferraro, Giovanna & Iovanella, Antonio, 2019. "Measuring network resilience through connection patterns," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 320-329.
    6. Liu, Xing & Fang, Yi-Ping & Zio, Enrico, 2021. "A Hierarchical Resilience Enhancement Framework for Interdependent Critical Infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    7. Fu, Shanshan & Yu, Yuerong & Chen, Jihong & Xi, Yongtao & Zhang, Mingyang, 2022. "A framework for quantitative analysis of the causation of grounding accidents in arctic shipping," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    8. Niels Bugert & Rainer Lasch, 2023. "Analyzing upstream and downstream risk propagation in supply networks by combining Agent-based Modeling and Bayesian networks," Journal of Business Economics, Springer, vol. 93(5), pages 859-889, July.
    9. Xu, Sheng & Kim, Ekaterina & Haugen, Stein & Zhang, Mingyang, 2022. "A Bayesian network risk model for predicting ship besetting in ice during convoy operations along the Northern Sea Route," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    10. Cassottana, Beatrice & Shen, Lijuan & Tang, Loon Ching, 2019. "Modeling the recovery process: A key dimension of resilience," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    11. David J. Yu & Michael L. Schoon & Jason K. Hawes & Seungyoon Lee & Jeryang Park & P. Suresh C. Rao & Laura K. Siebeneck & Satish V. Ukkusuri, 2020. "Toward General Principles for Resilience Engineering," Risk Analysis, John Wiley & Sons, vol. 40(8), pages 1509-1537, August.
    12. Sun, Hao & Yang, Ming & Wang, Haiqing, 2022. "A virtual experiment for measuring system resilience: A case of chemical process systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    13. Zhang, Chi & Zhang, Di & Zhang, Mingyang & Lang, Xiao & Mao, Wengang, 2020. "An integrated risk assessment model for safe Arctic navigation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 142(C), pages 101-114.
    14. Lan, He & Ma, Xiaoxue & Qiao, Weiliang & Deng, Wanyi, 2023. "Determining the critical risk factors for predicting the severity of ship collision accidents using a data-driven approach," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    15. Fang, Da & Guo, Yan, 2022. "Flow of goods to the shock of COVID-19 and toll-free highway policy: Evidence from logistics data in China," Research in Transportation Economics, Elsevier, vol. 93(C).
    16. Cheng-Hsien Hsieh, 2014. "Disaster risk assessment of ports based on the perspective of vulnerability," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 851-864, November.
    17. Cats, Oded & Jenelius, Erik, 2015. "Planning for the unexpected: The value of reserve capacity for public transport network robustness," Transportation Research Part A: Policy and Practice, Elsevier, vol. 81(C), pages 47-61.
    18. Sajid, Zaman, 2021. "A dynamic risk assessment model to assess the impact of the coronavirus (COVID-19) on the sustainability of the biomass supply chain: A case study of a U.S. biofuel industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    19. Giovanna Culot & Matteo Podrecca & Guido Nassimbeni & Guido Orzes & Marco Sartor, 2023. "Using supply chain databases in academic research: A methodological critique," Journal of Supply Chain Management, Institute for Supply Management, vol. 59(1), pages 3-25, January.
    20. Lutz Kilian & Nikos Nomikos & Xiaoqing Zhou, 2023. "Container Trade and the U.S. Recovery," International Journal of Central Banking, International Journal of Central Banking, vol. 19(1), pages 417-450, March.

    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:eee:reensy:v:241:y:2024:i:c:s0951832023005343. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    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.