An advanced risk analysis approach for container port safety evaluation

被引:83
|
作者
Alyami, Hani [1 ]
Lee, Paul Tae-Woo [2 ]
Yang, Zaili [1 ]
Riahi, Ramin [1 ]
Bonsall, Stephen [1 ]
Wang, Jin [1 ]
机构
[1] Liverpool John Moores Univ, Liverpool Logist Offshore & Marine LOOM Res Inst, Liverpool L3 5UX, Merseyside, England
[2] Soochow Univ, Dept Business Adm, Taipei, Taiwan
关键词
EVIDENTIAL REASONING APPROACH; SUPPLY CHAIN; SECURITY ASSESSMENT; FUZZY-SETS; MANAGEMENT; TERMINALS; METHODOLOGY; OPERATIONS; ACCIDENTS; SYSTEMS;
D O I
10.1080/03088839.2014.960498
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
Risk analysis in seaports plays an increasingly important role in ensuring port operation reliability, maritime transportation safety and supply chain distribution resilience. However, the task is not straightforward given the challenges, including that port safety is affected by multiple factors related to design, installation, operation and maintenance and that traditional risk assessment methods such as quantitative risk analysis cannot sufficiently address uncertainty in failure data. This paper develops an advanced Failure Mode and Effects Analysis (FMEA) approach through incorporating Fuzzy Rule-Based Bayesian Networks (FRBN) to evaluate the criticality of the hazardous events (HEs) in a container terminal. The rational use of the Degrees of Belief (DoB) in a fuzzy rule base (FRB) facilitates the implementation of the new method in Container Terminal Risk Evaluation (CTRE) in practice. Compared to conventional FMEA methods, the new approach integrates FRB and BN in a complementary manner, in which the former provides a realistic and flexible way to describe input failure information while the latter allows easy updating of risk estimation results and facilitates real-time safety evaluation and dynamic risk-based decision support in container terminals. The proposed approach can also be tailored for wider application in other engineering and management systems, especially when instant risk ranking is required by the stakeholders to measure, predict and improve their system safety and reliability performance.
引用
收藏
页码:634 / 650
页数:17
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