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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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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).
    7. 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.
    8. 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).
    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).
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