Abstract
Project risks are mostly considered to be independent in risk management, ignoring interdependencies among them, which can lead to inappropriate risk assessment and reduced efficacy in risk treatment. This study introduces a new Monte Carlo simulation-based risk interdependency network model to support decision makers in assessing project risks and planning risk treatment actions more effectively. The Interpretive Structural Modeling method is integrated into this model to develop a hierarchical project risk interdependency network based on identified risks and their cause-effect relationships. The Monte Carlo method is used to model the stochastic behavior of risk occurrence and to generate numerous possible risk scenarios through simulation. To evaluate single risks and overall project risk level while considering risk interdependencies, five major risk indicators are therefore proposed, namely simulated occurrence probability of a risk, simulated local and global influence of a risk, as well as total risk loss and total risk propagation loss of a project. An additional sensitivity analysis is also included to examine the effects of input uncertainties of this model on risk assessment results. Moreover, two case studies are provided to demonstrate the application and effectiveness of the proposed model. The findings accentuate the significance of considering risk interdependencies in project risk assessment and validate the model's robustness and feasibility in risk management of projects with complex interrelated risks.
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