An advanced risk analysis approach for container port safety evaluation

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 traditional risk assessment methods such as quantitative risk analysis cannot sufficiently address uncertainty in failure data. This paper develops an advanced Failure Mode and Effects Analysis (FMEA) approach through incorporating Fuzzy Rule-Based Bayesian Networks (FRBN) to evaluate the criticality of the hazardous events (HEs) in a container terminal. The rational use of the Degrees of Belief (DoB) in a fuzzy rule base (FRB) facilitates the implementation of the new method in Container Terminal Risk Evaluation (CTRE) in practice. Compared to conventional FMEA methods, the new approach integrates FRB and BN in a complementary manner, in which the former provides a realistic and flexible way to describe input failure information while the latter allows easy updating of risk estimation results and facilitates real-time safety evaluation and dynamic 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

An integrated fuzzy risk assessment for seaport operations

Seaport operations are characterised by high levels of uncertainty, as a result their risk evaluation is a very challenging task. Much of the available data associated with the system’s operations is uncertain and ambiguous, requiring a flexible yet robust approach of handling both quantitative and qualitative data as well as a means of updating existing information as new data becomes available. Conventional risk modelling approaches are considered to be inadequate due to the lack of flexibility and an inappropriate structure for addressing the system’s risks. This paper proposes a novel fuzzy risk assessment approach to facilitating the treatment of uncertainties in seaport operations and to optimise its performance effectiveness in a systematic manner. The methodology consists of a fuzzy analytical hierarchy process, an evidential reasoning (ER) approach, fuzzy set theory and expected utility. The fuzzy analytical hierarchy process is used to analyse the complex structure of seaport operations and determine the weights of risk factors while ER is used to synthesise them. The methodology provides a robust mathematical framework for collaborative modelling of the system and allows for a step by step analysis of the system in a systematic manner. It is envisaged that the proposed approach could provide managers and infrastructure analysts with a flexible tool to enhance the resilience of the system in a systematic manner

Risk Assessment and Management of Petroleum Transportation Systems Operations

Petroleum Transportation Systems (PTSs) have a significant impact on the flow of crude oil within a Petroleum Supply Chain (PSC), due to the great demand on this natural product. Such systems are used for safe movement of crude and/or refined products from starting points (i.e. production sites or storage tanks), to their final destinations, via land or sea transportation. PTSs are vulnerable to several risks because they often operate in a dynamic environment. Due to this environment, many potential risks and uncertainties are involved. Not only having a direct effect on the product flow within PSC, PTSs accidents could also have severe consequences for the humans, businesses, and the environment. Therefore, safe operations of the key systems such as port, ship and pipeline, are vital for the success of PTSs. This research introduces an advanced approach to ensure safety of PTSs. This research proposes multiple network analysis, risk assessment, uncertainties treatment and decision making techniques for dealing with potential hazards and operational issues that are happening within the marine ports, ships, or pipeline transportation segments within one complete system. The main phases of the developed framework are formulated in six steps. In the first phase of the research, the hazards in PTSs operations that can lead to a crude oil spill are identified through conducting an extensive review of literature and experts’ knowledge. In the second phase, a Fuzzy Rule-Based Bayesian Reasoning (FRBBR) and Hugin software are applied in the new context of PTSs to assess and prioritise the local PTSs failures as one complete system. The third phase uses Analytic Hierarchy Process (AHP) in order to determine the weight of PTSs local factors. In the fourth phase, network analysis approach is used to measure the importance of petroleum ports, ships and pipelines systems globally within Petroleum Transportation Networks (PTNs). This approach can help decision makers to measure and detect the critical nodes (ports and transportation routes) within PTNs. The fifth phase uses an Evidential Reasoning (ER) approach and Intelligence Decision System (IDS) software, to assess hazards influencing on PTSs as one complete system. This research developed an advance risk-based framework applied ER approach due to its ability to combine the local/internal and global/external risk analysis results of the PTSs. To complete the cycle of this study, the best mitigating strategies are introduced and evaluated by incorporating VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and AHP to rank the risk control options. The novelty of this framework provides decision makers with realistic and flexible results to ensure efficient and safe operations for PTSs

Supply Chain Risk Management in the Container Liner Shipping Industry from a Strategic Point of View

One of the most significant current discussions in the container liner shipping industry (CLSI) is supply chain risk management (SCRM). In recent years, there has been an increasing interest in managing risk and reliability in the container supply chain from many viewpoints. This paper reviews the significant literature related to SCRM in the CLSI from a strategic point of view. By integrating the concept of the CLSI, the planning levels of container liner shipping and the concept of SCRM, questions have been raised about risk and uncertainty arising from the external environments (i.e. country-limited scope) and how can these factors influence the organisational reliability and capability of liner shipping operators (LSOs). Another question concerns how uncertain environments can influence the punctuality of containerships. So far, however, no research has been found that answered these questions which make further research is meaningful. For future research, this paper recommends an extensive assessment of a business environment-based risk and an evaluation of organizational reliability and capability of LSOs from the strategic point of view. Finally, it is worth mentioning that there is a research gap in both industry and academia on how to analyse and predict the punctuality of containerships (i.e. arrival and departure) under uncertain environments. Keywords: supply chain risk management, container liner shipping industry, business-environment based risk, organisational reliability and capability, punctuality

Decision Making Analysis for an Integrated Risk Management Framework of Maritime Container Port Infrastructure and Transportation Systems

This research proposes a risk management framework and develops generic risk-based decision-making, and risk-assessment models for dealing with potential Hazard Events (HEs) and risks associated with uncertainty for Operational Safety Performance (OSP) in container terminals and maritime ports. Three main sections are formulated in this study: Section 1: Risk Assessment, in the first phase, all HEs are identified through a literature review and human knowledge base and expertise. In the second phase, a Fuzzy Rule Base (FRB) is developed using the proportion method to assess the most significant HEs identified. The FRB leads to the development of a generic risk-based model incorporating the FRB and a Bayesian Network (BN) into a Fuzzy Rule Base Bayesian Network (FRBN) method using Hugin software to evaluate each HE individually and prioritise their specific risk estimations locally. The third phase demonstrated the FRBN method with a case study. The fourth phase concludes this section with a developed generic risk-based model incorporating FRBN and Evidential Reasoning to form an FRBER method using the Intelligence Decision System (IDS) software to evaluate all HEs aggregated collectively for their Risk Influence (RI) globally with a case study demonstration. In addition, a new sensitivity analysis method is developed to rank the HEs based on their True Risk Influence (TRI) considering their specific risk estimations locally and their RI globally. Section 2: Risk Models Simulations, the first phase explains the construction of the simulation model Bayesian Network Artificial Neural Networks (BNANNs), which is formed by applying Artificial Neural Networks (ANNs). In the second phase, the simulation model Evidential Reasoning Artificial Neural Networks (ERANNs) is constructed. The final phase in this section integrates the BNANNs and ERANNs that can predict the risk magnitude for HEs and provide a panoramic view on the risk inference in both perspectives, locally and globally. Section 3: Risk Control Options is the last link that finalises the risk management based methodology cycle in this study. The Analytical Hierarchal Process (AHP) method was used for determining the relative weights of all criteria identified in the first phase. The last phase develops a risk control options method by incorporating Fuzzy Logic (FL) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to form an FTOPSIS method. The novelty of this research provides an effective risk management framework for OSP in container terminals and maritime ports. In addition, it provides an efficient safety prediction tool that can ease all the processes in the methods and techniques used with the risk management framework by applying the ANN concept to simulate the risk models

Safety evaluation of the ports along the maritime silk road

21st Century Maritime Silk Road (MSR) is of significant importance for world freight transport. The ports along the MSR present a key element of the involved shipping networks to support the connectivity of the MSR. Therefore, it is crucial to carry an effective safety assessment of the ports to ensure the robustness and sustainability of the growing MSR. However, traditional quantitative risk analysis approaches (QRA) used in ports face many challenges when being applied within the context of the MSR, such as risk data incompleteness and ambiguity, and operational and environmental uncertainties. This paper proposes a novel safety evaluation approach to address these issues encountered during the risk analysis process in the MSR ports. The fuzzy set theory (FST), an evidential reasoning (ER) approach, and the expected utility theory are integrated in a holistic way in the proposed methodology. The proposed methodology is used to analyse five key ports along the MSR. The results provide decision-makers with useful insights on enhancing port safety, effective route planning as well as improving operational efficiency

Advanced risk management in offshore terminals and marine ports

This research aims to propose a Risk Management (RM) framework and develop a generic risk-based model for dealing with potential hazards and risk factors associated with offshore terminals' and marine ports' operations and management. Hazard identification was conducted through an appropriate literature review of major risk factors of these logistic infrastructures. As a result in the first phase of this research a Fuzzy Analytical Hierarchal Process (FAHP) method was used for determining the relative weights of the risk factors identified via the literature review. This has led to the development of a generic risk -based model which can help related industrial professionals and risk managers assess the risk factors and develop appropriate strategies to take preventive/corrective actions for mitigation purposes, with a view of maintaining efficient offshore terminals' and marine ports' operations and management. In the second phase of the research the developed risk-based model incorporating Fuzzy Set Theory (FST), an Evidential Reasoning (ER) approach and the IDS software were used to evaluate the risk levels of different ports in real situations using a case study. The IDS software based on an ER approach was used to aggregate the previously determined relative weights of the risk factors with the new evaluation results of risk levels for the real ports. The third phase of the research made use of the Cause and Consequence Analysis (CCA) including the Fault Tree Analysis (FTA) and Event Tree Analysis (ETA) under a fuzzy environment, to analyse in detail the most significant risk factors determined from the first phase of the research, using appropriate case-studies. In the fourth phase of the research an individual RM strategy was tailored and implemented on the most significant risk factor identified previously. In the last phase of the research and in order to complete the RM cycle, the best mitigation strategies were introduced and evaluated in the form of ideal solutions for mitigating the identified risk factors. All methods used in this research have quantitative and qualitative nature. Expert judgements carried out for gathering the required information accounted for the majority of data collected. The proposed RM framework can be a useful method for managers and auditors when conducting their RM programmes in the offshore and marine industries. The novelty of this research can help the Quality, Health, Safety, Environment and Security (QHSES) managers, insurers and risk managers in the offshore and marine industries investigate the potential hazards more appropriately if there is uncertainty of data sources. In this research with considering strategic management approaches to RM development the proposed RM framework and risk based model contribute to knowledge by developing and evaluating an effective methodology for future use of the RM professionals

Risk analysis of petroleum transportation using fuzzy rule-based Bayesian reasoning

Petroleum transportation systems (PTSs) play a critical role in the movement of crude oil from its production sites to end users. Such systems are complex because they often operate in a dynamic environment. Safe operations of the key components in PTSs such as port and shipping are vital for the success of the systems. Risk assessment is a powerful tool to ensure the safe transportation of crude oil. This paper applies a mathematical model to identify and evaluate the operational hazards associated with PTSs, by incorporating a fuzzy rule-based (FRB) method with Bayesian networks (BNs). Its novelty lies in the realisation of risk analysis and prioritisation of the hazards in PTSs when historical failure data is not available. This hybrid model is capable of assisting decision-makers in measuring and improving the PTSs' safety, and dealing with the inherent uncertainties in risk data