Improved simulated annealing based risk interaction network model for project risk response decisions

作者:

Highlights:

• Integrate network simulation model and improved simulated annealing algorithm to optimize risk response decisions.

• Form a simulation model of capturing project risk interaction for evaluating risk response decisions.

• Propose an improved simulated annealing algorithm by enhancing its neighborhood search using social network analysis.

• Design two indices of social network analysis to find the key nodes and edges in the risk interaction network.

摘要

Risk interaction changes the probability and impact of a given risk, which may result in a less effective risk response decision (RD). This study presents an approach for supporting the project manager in making RDs, comprising a simulation model of risk interaction network (RIN) for evaluating the RDs and an improved simulated annealing (SA) algorithm for optimizing the RDs. The simulation model considers different risk levels and the corresponding risk interaction cases, which is closer to the reality. In addition to tailoring the SA algorithm to optimize RDs, it is improved through enhancing its neighborhood search with the aid of social network analysis. Specifically, two new network indices are designed for calculating the quantitative significance of RIN elements, i.e. the nodes that denote risks and edges that reflect the risk interactions. The element with a higher significance is more likely to be dealt with when generating a new RD in the neighborhood search. An application is provided to illustrate the utility of the proposed approach; a contrastive analysis of the improved SA and standard SA is also conducted to validate the effectiveness and efficiency of the former.

论文关键词:Project risk response,Network dynamic analysis,Risk interaction,Social network analysis,Simulated annealing

论文评审过程:Received 24 October 2018, Revised 3 May 2019, Accepted 5 May 2019, Available online 9 May 2019, Version of Record 4 July 2019.

论文官网地址:https://doi.org/10.1016/j.dss.2019.05.002