This paper aims to analyze the different concepts of "vulnerability" used in maritime supply chains, and to develop a novel framework with supporting models to identify and analyze the relevant vulnerabilities in the chains. A real case of the Maersk shipping line in its Asia-Europe route is studied to demonstrate the applicability of the proposed framework. We find that the investigated network has stronger robustness against random failures than that when facing deliberate attacks. Furthermore, to identify vulnerable nodes (i.e. ports) of the network, two different types of analysis are undertaken through a multicentrality model and a robustness analysis model, respectively. Consequently, the vulnerabilities estimated through robustness analysis can ascertain those by the classical centrality methods when they appear on both analysis results. More importantly, the similarity between the two outcomes can help gain more confidence on the accuracy in terms of the identification of the vulnerabilities in the system, while the difference (if any) such as those identified by the robustness analysis but not by the centrality analysis (or vice versa) can trigger a further investigation to find the comprehensive vulnerable nodes against different threats/hazards. It will aid rational decision on design and operation of resilient and robust maritime supply chains.

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Expert knowledge has been proved by substantial studies to be contributory to higher forecasting performance; meanwhile, its application is criticized and opposed by some groups for biases and inconsistency inherent in experts’ subjective judgment. This paper proposes a new approach to improving forecasting performance, which takes advantage of expert knowledge by constructing a constraint equation rather than directly adjusting the predicted values by experts. For the comparison purpose, the proposed approach, together with several widely used models including ARIMA, BP-ANN and the judgment model (JM), is applied to forecasting the container throughput of Guangzhou Port, which is one of the most important ports of China. Forecasting performances of the above models are compared and the results clearly show superiority of the proposed approach over its rivals, which implies that expert knowledge will make positive contribution as long as it is used in a right way.

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