Bi-objective optimization of the scheduling of risk-related resources for risk response

Risk response is a crucial component of project risk management, playing a key role in mitigating the adverse effects of risks. However, the implementation of such risk response relies on the utilization of appropriate riskrelated resources. Given the scarcity of these resources, their proper scheduling and allocation are critical. To address this issue, this paper introduces a flow-based continuous-time bi-objective optimization model for riskrelated resource planning. The proposed model is then applied to a case study, deriving the global optimal solution based on the tuned parameters. Moreover, we develop a tailored rule-based metaheuristic algorithm for the model. The algorithm incorporates improved random key-based population initialization and a rule-based genetic operator, facilitating the application of the proposed model to large-scale projects. The computational results of the case study and numerical experiments not only validate the effectiveness of the algorithm, but also emphasize the importance of the precautionary principle and redundant resources in project risk management.

Automated summary

Risk response is an essential part of project risk management, which helps reduce the negative impacts of risks. The paper proposes a flow-based continuous-time bi-objective optimization model for risk-related resource planning in response to the scarcity of resources. The model is successfully applied to a case study, obtaining the global optimal solution through parameter tuning. Additionally, a rule-based metaheuristic algorithm is developed to handle large-scale projects by incorporating improved population initialization and genetic operators. The computational results of the case study and numerical experiments validate the effectiveness of the algorithm and highlight the significance of the precautionary principle and redundant resources in project risk management.