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Risk Management in Projects by Bayesian Belief Networks

Chitgar, Siamak | 2015

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 51876 (51)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Haji, Alireza
  7. Abstract:
  8. Risk management is one of the important phases of a project life cycle. There are several quantitative and qualitative methods to determine and plan the proper reaction risks in a project. These methods aim to prevent additional and unnecessary costs. Project managers, stake holders and beneficiaries always believe that risk management of project engineering as one of the importance parts. Although there are multiple methods that can evaluate risk, but there is still research problems especially for risk qualitative and quantitative prediction. This gets more serious after evaluating and minimizing risks effects considering the other risks’ damaging effects which have not been considered. In this project, after studying and evaluating various risk prevention and control methods, we have tried to propose a quantitative method for finding the risks and their effects using BBN (Bayesian belief networks ). The thesis has conducted a comprehensive case study to test the proposed method on a real project (oil filed upstream). In this case study as a field operation, we are unable to identify risk factors early and easy. So for an effective planning, the field needs to know the important risks and their effects on a project results. These effects depend on quantitative data from previous projects and give us a better understanding of risks natures and their effects on the project. After proper data were accumulated, by using BBN we Identified the risk with the highest impact, the Bayesian networks have been made and the results were studied. The demonstration of sensitivity analysis have shown the correlation of probable risks on other uncertainties and risk factors
  9. Keywords:
  10. Risk Management ; Risk Analysis ; Uncertainty ; Risk Evalution ; Risk Factors ; Bayesian Network

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