Oil terminals are complex infrastructures due to their diverse operational activities. They are exposed to diverse risks because they usually operate in a dynamic environment in which safety barriers are sometime overwhelmed, leading to the disruption of operations due to a high level of uncertainty. However, the ability of oil terminals to minimise vulnerability and maximise resilience depends on the availability of the correct anticipated information at the right time for a decision-making process. An important finding from the reviewed literature revealed that uncertainties and the unpredictability of the convergent effect of several hazardous factors have the potential to cause major disruptions such as fire, explosion and transit accidents. The consequences of these disruptions can lead to infrastructure damage and loss of life. The common operational threats to oil terminal operations (OTOs) substantiates the need for a holistic resilience model for operations in offshore/onshore terminals such as berthing/unberthing, vessel manoeuvring, loading and offloading, storage, etc. Due to the uncertainties associated with these operations and the cases of reported incidents/accidents, this research focuses more on the aspect of loading and offloading operations at ship/terminal interface. An emphasis on a resilience modelling approach provides a flexible yet robust model for OTOs to address disruption proactively, particularly with constantly evolving hazards and threats. This thesis introduces an innovative approach towards resilience modelling based on a developed novel framework. The key aspect of the framework was supported using three proposed models: (1) the integration of Utility Theory and Swiss Cheese Model (UtiSch_+), to evaluate the relative importance of the identified hazard factors (HFs), (2) a Bayesian network (BN), to calculate the overall probability that a specific hazard is present and, (3) an Analytical Hierarchical Process (AHP) - Prospect Theory (PT) approach, as an important model for a strategic decision selection method. An empirical study was conducted to test the validity the proposed models, using case studies and Sensitivity Analysis (SA). The result obtained demonstrated that the models are effective techniques to obtain the relative weight of the identified Hazard Factors (HFs) in order to prioritise them, for dynamic hazards probability evaluation and to prioritise suggested resilience strategies in order of importance to mitigate hazard/risk level. Evidently, the result revealed appears reasonable and appropriate for investment, in order to support a strategic decision for the selection of a resilience strategy for resilience improvement in OTOs.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:718882 |
| Date | January 2017 |
| Creators | Usman, A. Y. |
| Contributors | Ren, J. ; Wang, J. ; Jones, K. |
| Publisher | Liverpool John Moores University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://researchonline.ljmu.ac.uk/6602/ |
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