title: Advanced uncertainty modelling for container port risk analysis.
abstract: 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.
authors.affiliation: Liverpool Logistics Offshore and Marine (LOOM) Research Institute, Liverpool John Moores University, Liverpool, UK.
authors.forename: Hani
authors.initials: H
authors.lastname: Alyami
authors.affiliation: Liverpool Logistics Offshore and Marine (LOOM) Research Institute, Liverpool John Moores University, Liverpool, UK. Electronic address: z.yang@ljmu.ac.uk.
authors.forename: Zaili
authors.initials: Z
authors.lastname: Yang
authors.affiliation: Liverpool Logistics Offshore and Marine (LOOM) Research Institute, Liverpool John Moores University, Liverpool, UK.
authors.forename: Ramin
authors.initials: R
authors.lastname: Riahi
authors.affiliation: Liverpool Logistics Offshore and Marine (LOOM) Research Institute, Liverpool John Moores University, Liverpool, UK.
authors.forename: Stephen
authors.initials: S
authors.lastname: Bonsall
authors.affiliation: Liverpool Logistics Offshore and Marine (LOOM) Research Institute, Liverpool John Moores University, Liverpool, UK.
authors.forename: Jin
authors.initials: J
authors.lastname: Wang
mesh_terms: D001499:Bayes Theorem; D003132:Commerce; D006801:Humans; D038622:Internationality; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012767:Ships; D035501:Uncertainty
keywords: FMEA; Maritime risk; Maritime safety; Maritime transport; Port safety; Sensitivity analysis