IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i15p9338-d876342.html
   My bibliography  Save this article

A Model to Evaluate the Effectiveness of the Maritime Shipping Risk Mitigation System by Entropy-Based Capability Degradation Analysis

Author

Listed:
  • Jun Shen

    (School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China)

  • Xiaoxue Ma

    (School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China)

  • Weiliang Qiao

    (Marine Engineering College, Dalian Maritime University, Dalian 116026, China)

Abstract

Accurate evaluation of the risk mitigation status of navigating ships is essential for guaranteeing navigational safety. This research mainly focuses on the feasibility and accuracy of evaluating the real effectiveness of a risk mitigation system for navigating ships, including addressing the problem of immeasurableness for risk mitigation capability and determining the degradation regulation of risk mitigation capability over time. The proposed method to solve the problem is an effectiveness evaluation model based on the capability perspective, composed of a capability measurement algorithm based on entropy theory and capability degradation regulation analysis based on numerical process fitting. First, combined with the theoretical framework of a comprehensive defence system, the risk mitigation system designed for navigating ships is reconstructed based on capability building. Second, using a numerical fitting method, the degradation regulation of risk mitigation capability with time is obtained to improve the accuracy of the dynamic evaluation. Finally, referring to entropy theory, the uncertainty of capability is calculated, and then the model is constructed based on this uncertainty to realize the effectiveness evaluation from a capability perspective. The results obtained in an application test of the proposed model indicate that using the entropy of capability can realize an accurate effectiveness evaluation of a risk mitigation system for navigating ships, with a 9% improvement in accuracy, and the Weibull curve fitting is more consistent with capability degradation regulation, with a signification level of 2.5%. The proposed model provides a new path for evaluating the effectiveness of a risk mitigation system for navigating ships from the entropy of capability, and compared with the traditional probabilistic method, the model is more realistic and accurate in actual applications.

Suggested Citation

  • Jun Shen & Xiaoxue Ma & Weiliang Qiao, 2022. "A Model to Evaluate the Effectiveness of the Maritime Shipping Risk Mitigation System by Entropy-Based Capability Degradation Analysis," IJERPH, MDPI, vol. 19(15), pages 1-34, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9338-:d:876342
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/15/9338/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/15/9338/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Aslett, Louis J.M. & Nagapetyan, Tigran & Vollmer, Sebastian J., 2017. "Multilevel Monte Carlo for Reliability Theory," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 188-196.
    2. Gao, Hongda & Cui, Lirong & Kong, Dejing, 2018. "Reliability analysis for a Wiener degradation process model under changing failure thresholds," Reliability Engineering and System Safety, Elsevier, vol. 171(C), pages 1-8.
    3. Lewis, Herbert F. & Lock, Kathleen A. & Sexton, Thomas R., 2009. "Organizational capability, efficiency, and effectiveness in Major League Baseball: 1901-2002," European Journal of Operational Research, Elsevier, vol. 197(2), pages 731-740, September.
    4. Jia, Xiaohui & Zhang, Donghui, 2021. "Prediction of maritime logistics service risks applying soft set based association rule: An early warning model," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    5. Shiqi Fan & Zaili Yang & Eduardo Blanco-Davis & Jinfen Zhang & Xinping Yan, 2020. "Analysis of maritime transport accidents using Bayesian networks," Journal of Risk and Reliability, , vol. 234(3), pages 439-454, June.
    6. Degang Wang & Dongling Li & Yujuan Dou & Yonghua Tan & Zhenke Zhang & Dui’an Lv & Jie Cheng & Naeem Jan, 2022. "Fuzzy Comprehensive Evaluation of Maritime Boundary Delimitation Schemes Based on AHP-Entropy Weight Method," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-13, May.
    7. Kayisoglu, Gizem & Gunes, Bunyamin & Besikci, Elif Bal, 2022. "SLIM based methodology for human error probability calculation of bunker spills in maritime operations," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    8. Yu, Qing & Teixeira, Ângelo Palos & Liu, Kezhong & Rong, Hao & Guedes Soares, Carlos, 2021. "An integrated dynamic ship risk model based on Bayesian Networks and Evidential Reasoning," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    9. Fan, Shiqi & Blanco-Davis, Eduardo & Yang, Zaili & Zhang, Jinfen & Yan, Xinping, 2020. "Incorporation of human factors into maritime accident analysis using a data-driven Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    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. Sezer, Sukru Ilke & Akyuz, Emre & Arslan, Ozcan, 2022. "An extended HEART Dempster–Shafer evidence theory approach to assess human reliability for the gas freeing process on chemical tankers," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    2. Antão, P. & Sun, S. & Teixeira, A.P. & Guedes Soares, C., 2023. "Quantitative assessment of ship collision risk influencing factors from worldwide accident and fleet data," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    3. Zhang, Jinfeng & Jin, Mei & Wan, Chengpeng & Dong, Zhijie & Wu, Xiaohong, 2024. "A Bayesian network-based model for risk modeling and scenario deduction of collision accidents of inland intelligent ships," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    4. Li, Huanhuan & Ren, Xujie & Yang, Zaili, 2023. "Data-driven Bayesian network for risk analysis of global maritime accidents," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    5. 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).
    6. Rong, H. & Teixeira, A.P. & Guedes Soares, C., 2022. "Maritime traffic probabilistic prediction based on ship motion pattern extraction," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    7. Guo, Yunlong & Jin, Yongxing & Hu, Shenping & Yang, Zaili & Xi, Yongtao & Han, Bing, 2023. "Risk evolution analysis of ship pilotage operation by an integrated model of FRAM and DBN," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    8. Xiaoyuan Zhao & Haiwen Yuan & Qing Yu, 2021. "Autonomous Vessels in the Yangtze River: A Study on the Maritime Accidents Using Data-Driven Bayesian Networks," Sustainability, MDPI, vol. 13(17), pages 1-17, September.
    9. Zhou, Jian-Lan & Yu, Ze-Tai & Xiao, Ren-Bin, 2022. "A large-scale group Success Likelihood Index Method to estimate human error probabilities in the railway driving process," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    10. Domeh, Vindex & Obeng, Francis & Khan, Faisal & Bose, Neil & Sanli, Elizabeth, 2023. "An operational risk awareness tool for small fishing vessels operating in harsh environment," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    11. Gao, Dawei & Zhu, Yongsheng & Guedes Soares, C., 2023. "Uncertainty modelling and dynamic risk assessment for long-sequence AIS trajectory based on multivariate Gaussian Process," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    12. Lan, He & Ma, Xiaoxue & Ma, Laihao & Qiao, Weiliang, 2023. "Pattern investigation of total loss maritime accidents based on association rule mining," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    13. Ung, S.T., 2021. "Navigation Risk estimation using a modified Bayesian Network modeling-a case study in Taiwan," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    14. Wu, Bing & Yip, Tsz Leung & Yan, Xinping & Guedes Soares, C., 2022. "Review of techniques and challenges of human and organizational factors analysis in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    15. Chang, Miaoxin & Huang, Xianzhen & Coolen, Frank P.A. & Coolen-Maturi, Tahani, 2021. "Reliability analysis for systems based on degradation rates and hard failure thresholds changing with degradation levels," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    16. Zhang, Ao & Wang, Zhihua & Bao, Rui & Liu, Chengrui & Wu, Qiong & Cao, Shihao, 2023. "A novel failure time estimation method for degradation analysis based on general nonlinear Wiener processes," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    17. Yang, Meide & Zhang, Dequan & Jiang, Chao & Han, Xu & Li, Qing, 2021. "A hybrid adaptive Kriging-based single loop approach for complex reliability-based design optimization problems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    18. Xu, Qinqin & Zhu, Yuanguo, 2022. "Reliability modeling of uncertain random fractional differential systems with competitive failures," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    19. Wang, Lei & Liu, Qing & Dong, Shiyu & Guedes Soares, C., 2022. "Selection of countermeasure portfolio for shipping safety with consideration of investment risk aversion," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    20. Liu, Zhichen & Li, Ying & Zhang, Zhaoyi & Yu, Wenbo, 2022. "A new evacuation accessibility analysis approach based on spatial information," Reliability Engineering and System Safety, Elsevier, vol. 222(C).

    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:jijerp:v:19:y:2022:i:15:p:9338-:d:876342. 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.