Model-Based Risk Assessment in Multi-disciplinary Systems Engineering

Biffl, Stefan; Berardinelli, Luca; Maetzler, Emanuel; Wimmer, Manuel; Lüeder, Arndt; Schmidt, Nicole

doi:10.1109/seaa.2015.75

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Evolution, as a dynamic assessment tool, plays a crucial role in understanding the trends in risks and improving decision efficiency [12]. Based on the principles of risk evolution, Biffl et al [13] introduced a risk assessment model for software and systems engineering projects. Taking into account the impact of uncertainty in project management on risk evolution [15], Wang et al [14] selected typical failure evolution processes and constructed a dynamic simulation model for risk evolution based on system dynamics.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Risk evolution is a tool that can capture the dynamic characteristics of risks and improve the scientificity of decision-making in risk management [12]. The existing research on risk evolution mainly focused on a single project [13][14][15][16], whereas the exploration among projects in the PP is very limited. These studies regarded the projects as risk carriers and assigned different risk attributes to them.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of Project Portfolio Risk Evolution and Response under the Influence of Interactions

Zhang,

Liu,

Xie

et al. 2023

Mathematics

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Due to dynamic changes in both internal and external environments, the risk evolution of the project portfolio (PP) becomes extremely complicated, thereby increasing the difficulties of effective risk response. In particular, the real-time influence of risk interactions on the evolution of project portfolio risk (PPR) often goes unnoticed. Meanwhile, risk contagiousness is completely ignored in risk response. To tackle this challenge, this study proposes a PPR evolution and response (PPRER) model by improving the Barrat–Barthelemy–Vespignani (BBV) model and by introducing the evolutionary dynamics method into the PPR response research. The feasibility and applicability of the proposed model are verified through a numerical illustration. Computational results demonstrate that the proposed model can simulate the evolution process of PPRs under the influence of their interactions and give a snapshot of the real-time interactive relationship between PPRs. Based on the obtained results, decision-makers can take effective risk responses by identifying critical strategy intrusion nodes at any time in the evolution process.

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Section: Literature Reviewmentioning

confidence: 99%