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

Abstract

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.

External stakeholder risk response strategies selection in project portfolio

“… The WBS-based method [ 21 , 40 ] Focus on problem-solving rather than seeking optimal solutions. The optimization-model method [ 12 , 15 , 21 , 46 ] Multiple objectives can be considered, and optimal solutions can be found quantitatively. …”

A comprehensive risk prediction method for defense mission planning based on probabilistic reasoning and hierarchical analysis

“…The impact value of the other tasks on the 𝑖th link task 15 𝑗=1 𝑒 𝑖 𝑗 + (𝑁 − 1) The impact value of the 𝑖th link task on other tasks (16) Taking 15 𝑖=1 𝑒 𝑖 𝑗 + (𝑁 − 1) and 15 𝑗=1 𝑒 𝑖 𝑗 + (𝑁 − 1) as the horizontal and vertical coordinates of the 𝑖th link task, respectively, the possibility hierarchy position distribution of each link task of 𝐹 1 ∼ 𝐹 15 can be calculated, as shown in Figure 5. Among them, "0, 1, 2, 3, 4" on the coordinate axis represent the boundaries of the four layer task hierarchy established in the defense task planning process, and their physical meaning is the number of hierarchical levels.…”

“…Most of the above research results focus on constructing risk element sets and analyzing their uncertainty; however, comprehensive prediction is needed on the premise that risk factors are independent. Subsequently, researchers have made significant efforts in comprehensive risk prediction based on factor associations; e.g., Zhang et al [16] proposed the project portfolio risk (PPR) evolution and response model to solve the problem of project risk interaction, which effectively reflected the real-time interaction in the evolution process of PPRs and helped decision makers quickly identify key strategic intrusion nodes [16] . Zhang et al [16] proposed a category-based association measurement technology (the Measuring Attractiveness by a Categorical-Based Evaluation Technique (MACBETH)) to measure the associative relationships between risks and construct a risk response model for the selection of risk relationships in the medical system research and development project (Risk Response Actions (RRA)), so as to maximize the expectations of the management of medical system research and development project under budget constraints [5] .…”

“…Subsequently, researchers have made significant efforts in comprehensive risk prediction based on factor associations; e.g., Zhang et al [16] proposed the project portfolio risk (PPR) evolution and response model to solve the problem of project risk interaction, which effectively reflected the real-time interaction in the evolution process of PPRs and helped decision makers quickly identify key strategic intrusion nodes [16] . Zhang et al [16] proposed a category-based association measurement technology (the Measuring Attractiveness by a Categorical-Based Evaluation Technique (MACBETH)) to measure the associative relationships between risks and construct a risk response model for the selection of risk relationships in the medical system research and development project (Risk Response Actions (RRA)), so as to maximize the expectations of the management of medical system research and development project under budget constraints [5] . Abedzadeh et al used fuzzy decision tree analysis to determine the possible combinatorial relationships among social, economic, environmental, and water damage index attributes to achieve an integrated risk management decision for developing water resources [17] .…”