Assessment of port resilience using Bayesian network: A study of strategies to enhance readiness and response capacities

Nanxi Wang, Min Wu and Kum Fai Yuen

Reliability Engineering and System Safety, 2023, vol. 237, issue C

Abstract: Ports, as critical infrastructure, are a crucial part of the maritime transportation system (MTS) and are increasingly vulnerable to disturbances and disasters in the post-pandemic era. Resilience plays a crucial role in building sustainable infrastructure, and developing a resilient port system is a priority in planning and developing MTS. This paper aims to develop measures to enhance port resilience that can cope with risks and uncertainties. A circular four-stage method is proposed to study port resilience. The major disturbances that are currently affecting ports are summarized and classified. Then, a port resilience assessment model using the Bayesian network is proposed, in which various resilience strategies are categorized into different metrics to assess resilience capabilities (i.e. readiness and response capacities). The model is constructed based on expert judgment and statistical analysis. The Shanghai Yangshan Deepwater Port in China is used as a case study. The results show that natural disasters are major disruptors plaguing ports. The overall resilience of automated terminals is higher than that of non-automated terminals. Strategies to enhance visibility, such as building real-time data management systems and data analysis programs, have the most significant impact on improving ports’ readiness. Strategies to enhance recovery such as facility restoration and technology restoration are the most important when improving ports’ response capability.

Keywords: Port resilience; Bayesian network; Maritime transportation system; Strategy implementation; Resilience assessment (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:237:y:2023:i:c:s0951832023003083

DOI: 10.1016/j.ress.2023.109394

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