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Hindi A Quantitative Risk Management Methodology: The Case of Offshore LNG Terminals and Marine Ports
*Corresponding Author: Kambiz Mokhtari, ECO College of Insurance, Allameh Tabataba’i University, Tehran, Iran, Tel: +98 21 88770018, Email: kambiz.mokhtari@atu.ac.irReceived Date: Feb 21, 2019 / Accepted Date: Mar 18, 2019 / Published Date: Mar 25, 2019
Citation: Mokhtari K (2019) A Quantitative Risk Management Methodology: The Case of Offshore LNG Terminals and Marine Ports. J Marine Sci Res Dev 9: 268.
Copyright: © 2019 Mokhtari K. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
In marine and offshore industry although there has been major development towards loss prevention concepts such as internal and external audits, inspections, surveys, upgrades, maintenance, physical and technical modifications, enforcing new regulations via united nation’s agencies such as IMO and ILO and technical standards via classification societies to avoid the potential hazards and risks of damage to assets e.g. fixed offshore structures and environment or harming people etc., but the moves toward managing the hazards and risks in a methodological way which are linked directly to the management and decision making processes have been very slow. Furthermore, in marine and offshore industry most perceptions, frameworks and methodologies of dealing with hazards and risks are for their assessment rather than their management. This trend reveals the fact that in different marine and offshore industry sectors such as logistics, oil and gas there is a lack of coherent Quantitative Risk Management (QRM) methodology from which to understand the risk-based decisions especially for the purpose of appropriate risk management e.g. offshore terminals and marine ports. Therefore, in this paper initially, Fuzzy Set Theory was applied to deal with vagueness of the uncertain risk-based data. In the next stage Fuzzy Fault Tree and Fuzzy Event Tree methods were used to achieve the sequence of quantitative risk analysis. In the final step a Fuzzy TOPSIS model was used for implementation of the mitigation phase. Finally, the practicability of the addressed QRM methodology under Fuzzy Environment was verified with the use of a suitable case study.
