Modelling the reliability of search operations within the UK through Bayesian belief networks

This paper uses a Bayesian belief networks (BBN) methodology to assess the reliability of search and rescue (SAR) operations within the UK coastguard (maritime rescue) coordination centers. This is an extension of earlier work, which investigated the rationale of the government's decision to close a number of coordination centers. The previous study made use of secondary data sources and employed a binary logistic regression methodology to support the analysis. This study focused on the collection of primary data through a structured elicitation process, which resulted in the construction of a BBN. The main findings of the study are that approaches such as logistic regression are complementary to BBN's. The former provided a more objective assessment of associations between variables but was restricted in the level of detail that could be explicitly expressed within the model due to lack of available data. The latter method provided a much more detailed model but the validity of the numeric assessments was more questionable. Each method can be used to inform and defend the development of the other. The paper describes in detail the elicitation process employed to construct the BBN and reflects on the potential for bias

Predicting a Containership's Arrival Punctuality in Liner Operations by Using a Fuzzy Rule-Based Bayesian Network (FRBBN)

One of the biggest concerns in liner operations is punctuality of containerships. Managing the time factor has become a crucial issue in today's liner shipping operations. A statistic in 2015 showed that the overall punctuality for containerships only reached an on-time performance of 73%. However, vessel punctuality is affected by many factors such as the port and vessel conditions and knock-on effects of delays. As a result, this paper develops a model for analyzing and predicting the arrival punctuality of a liner vessel at ports of call under uncertain environments by using a hybrid decision-making technique, the Fuzzy Rule-Based Bayesian Network (FRBBN). In order to ensure the practicability of the model, two container vessels have been tested by using the proposed model. The results have shown that the differences between prediction values and real arrival times are only 4.2% and 6.6%, which can be considered as reasonable. This model is capable of helping liner shipping operators (LSOs) to predict the arrival punctuality of their vessel at a particular port of call. © 2017 The Korean Association of Shipping and Logistics, Inc

Metrics for Assessing Overall Performance of Inland Waterway Ports: A Bayesian Network Based Approach

Because ports are considered to be the heart of the maritime transportation system, thereby assessing port performance is necessary for a nation’s development and economic success. This study proposes a novel metric, namely, “port performance index (PPI)”, to determine the overall performance and utilization of inland waterway ports based on six criteria, port facility, port availability, port economics, port service, port connectivity, and port environment. Unlike existing literature, which mainly ranks ports based on quantitative factors, this study utilizes a Bayesian Network (BN) model that focuses on both quantitative and qualitative factors to rank a port. The assessment of inland waterway port performance is further analyzed based on different advanced techniques such as sensitivity analysis and belief propagation. Insights drawn from the study show that all the six criteria are necessary to predict PPI. The study also showed that port service has the highest impact while port economics has the lowest impact among the six criteria on PPI for inland waterway ports

Assessment of the Impact of Seafarers' Professional Experience on the Perception of Risk Factors from the Perspective of Polish Container Ships Crews Members

Safety of navigation is the important issue especially related to the dynamically developing container shipping. The main purpose of the article is to demonstrate the results of research on the perception of risk factors by seafarers working on container ships in terms of their professional experience. The ranking of risk factors considering their impact on the safety of container shipping has been created. An additional goal of the research was to acquire the knowledge on ships crews members' assessment of the impact of the human factor on the safety of navigation, including factors related to the organization of seafarers' work (systemic aspects) and those directly resulting from the operations carried out on ship. The assessment of risk factors affecting the navigation safety was performed from the perspective of Polish crew members working on container ships. The research was carried out with the use of an empirical study questionnaire. 161 seafarers' opinions were analyzed. On the basis of the created risk factors ranking analysis, it was found that seafarers perceive the human factor, and consider both the systemic and the work-related aspects having the greatest impact on the safety of container shipping. Moreover, the conducted non-parametric Pearson chi-square independence test proved the hypothesis that assessments of the five highest rated risk factors, reviewed by studied young and experienced professional groups of seafarers, didn’t differ significantly

Implementing Bayesian networks for ISO 31000:2018-based maritime oil spill risk management: State-of-art, implementation benefits and challenges, and future research directions

The risk of a large-scale oil spill remains significant in marine environments as international maritime transport continues to grow. The environmental as well as the socio-economic impacts of a large-scale oil spill could be substantial. Oil spill models and modeling tools for Pollution Preparedness and Response (PPR) can support effective risk management. However, there is a lack of integrated approaches that consider oil spill risks comprehensively, learn from all information sources, and treat the system uncertainties in an explicit manner. Recently, the use of the international ISO 31000:2018 risk management framework has been suggested as a suitable basis for supporting oil spill PPR risk management. Bayesian networks (BNs) are graphical models that express uncertainty in a probabilistic form and can thus support decision-making processes when risks are complex and data are scarce. While BNs have increasingly been used for oil spill risk assessment (OSRA) for PPR, no link between the BNs literature and the ISO 31000:2018 framework has previously been made. This study explores how Bayesian risk models can be aligned with the ISO 31000:2018 framework by offering a flexible approach to integrate various sources of probabilistic knowledge. In order to gain insight in the current utilization of BNs for oil spill risk assessment and management (OSRA-BNs) for maritime oil spill preparedness and response, a literature review was performed. The review focused on articles presenting BN models that analyze the occurrence of oil spills, consequence mitigation in terms of offshore and shoreline oil spill response, and impacts of spills on the variables of interest. Based on the results, the study discusses the benefits of applying BNs to the ISO 31000:2018 framework as well as the challenges and further research needs.Peer reviewe

Risk Assessment and Management for Maritime SAR and Oil Spill Response

This report summarizes selected publications that deal with maritime Search and Rescue (SAR) operations, winter navigation as well as oil spill response. In the first part of this report, the SAR capabilities, response times and effects of weather on Finnish Search and Rescue Units (SRUs) are evaluated. Besides this, the risk of oil spill and effects of winter conditions were evaluated. Two of the most relevant accident types – collisions consequences on oil tanker and RoPAx vessels – were evaluated. Both tankers as well as RoPax vessels are very common vessels in the Gulf of Finland, carrying thousands passengers or tonnes of oil. However, during the project it was found that there are currently no particularly reliable methods for assessing which sea areas are most prone to accidents. This highlights the need for future research in the methodology. Furthermore, a model is presented that describes the interaction between ships and the ice when navigating in an ice channel. This model helps to understand better the increased side forces and yaw that occurs in ice channel when compared to sailing in open waters. This can be used to train bridge personnel to better understand their ship's behavior under challenging ice channel conditions, thus decreasing risk.  A final model describes how fast an oil slick will spread in an ice channel as a function of factors such as the ice concentration and ice floe size, allowing for better estimation of how far oil will spread until effective clean-up measures can be taken