Over the last few years, there has been a growing international recognition that the security performance of the maritime industry needs to be reviewed on an urgent basis. A large number of optional maritime security control measures have been proposed through various regulations and publications in the post-9/11 era. There is a strong need for a sound and generic methodology, which is capable of taking into account multiple selection criteria such as the cost effectiveness of the measures based on reasonable security assessment. The use of traditional risk assessment and decision-making approaches to deal with potential terrorism threats in a maritime security area reveals two major challenges. They are lack of capability of analyzing security in situations of high-level uncertainty and lack of capability of processing diverse data in a utility form suitable as input to a risk inference mechanism. To deal with such difficulties, this article proposes a subjective security-based assessment and management framework using fuzzy evidential reasoning (ER) approaches. Consequently, the framework can be used to assemble and process subjective risk assessment information on different aspects of a maritime transport system from multiple experts in a systematic way. Outputs of this model can also provide decisionmakers with a transparent tool to evaluate maritime security policy options for a specific scenario in a cost-effective manner.

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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 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.

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Globalization has led to a rapid increase of container movements in seaports. Risks in seaports need to be appropriately addressed to ensure economic wealth, operational efficiency, and personnel safety. As a result, the safety performance of a Container Terminal Operational System (CTOS) plays a growing role in improving the efficiency of international trade. This paper proposes a novel method to facilitate the application of Failure Mode and Effects Analysis (FMEA) in assessing the safety performance of CTOS. The new approach is developed through incorporating a Fuzzy Rule-Based Bayesian Network (FRBN) with Evidential Reasoning (ER) in a complementary manner. The former provides a realistic and flexible method to describe input failure information for risk estimates of individual hazardous events (HEs) at the bottom level of a risk analysis hierarchy. The latter is used to aggregate HEs safety estimates collectively, allowing dynamic risk-based decision support in CTOS from a systematic perspective. The novel feature of the proposed method, compared to those in traditional port risk analysis lies in a dynamic model capable of dealing with continually changing operational conditions in ports. More importantly, a new sensitivity analysis method is developed and carried out to rank the HEs by taking into account their specific risk estimations (locally) and their Risk Influence (RI) to a port's safety system (globally). Due to its generality, the new approach can be tailored for a wide range of applications in different safety and reliability engineering and management systems, particularly when real time risk ranking is required to measure, predict, and improve the associated system safety performance.

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More than 80% of international cargo moves through seaports, making a contribution to the world economy. As a result, the performance of seafarers plays a major role in the safety of international trade and the maritime environment. This paper presents a novel approach to monitoring the performance of seafarers in terms of their conditional reliability. Unlike a traditional reliability analysis of a seafarer, this approach contains a dynamic model capable of coping with continually changing conditions that affect a seafarer's performance. The proposed methodology enables and facilitates decision makers to assess the performance of a seafarer before his or her designation to any activities and during his or her seafaring period. To evaluate a seafarer's reliability, a generic model is constructed and a combination of different techniques such as fuzzy logic, a fuzzy rule base, an analytical hierarchy process, evidential reasoning, a mapping process and expected utility is used. Furthermore, by changing the conditions that affect the reliability of an ideal seafarer and through calculating a value for this reliability, a benchmark is constructed. A seafarer's reliability depends upon many variables and their dependencies; alteration of a criterion value will ultimately alter a seafarer's reliability. In order to correct any deviation on time, a seafarer's reliability has to be measured appropriately and regularly.

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