Risk response budget allocation based on fault tree analysis and optimization

Budget allocation in project risk response is a vital issue in project risk management since it relates to a reasonable utilization of strict project budgets and effective mitigations of the risks that typify projects. This paper presents an integrated method based on an optimization model and fault tree analysis for allocating a risk response budget from a preventive and protective perspective. The proposed method consists of three main steps. The first step is to analyse and calculate risk probabilities and risk losses which involves identifying risk causes that may trigger a risk event to occur using fault tree analysis. It also identifies consequences once the risk event occurs, evaluates the occurrence probabilities of risk causes and expected financial losses of consequences. The second step is to build a relationship between the budget allocated to risk response strategies and the corresponding response effects. The third step is to construct an optimization model aiming at minimizing the total risk cost. We present proofs for the optimal risk response strategy in special cases of the budget allocation model. Furthermore, a detailed computational experiment is performed to gain insights into the three-phased budget allocation model for more general cases. The results show that an optimal risk response budget can be determined and the structure of the risk network has a significant impact on the preferred risk response strategy.

Automated summary

Budget allocation in project risk response is crucial for reasonable utilization of project budgets and effective risk mitigation. This paper proposes an integrated method based on optimization model and fault tree analysis. It includes three steps: analyzing risk probabilities and losses, establishing the relationship between budget and response effects, and constructing an optimization model to minimize total risk cost. The paper provides proofs for optimal risk response strategy and a computational experiment to support the proposed method.