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Risks and resulting propagating accidents pose considerable threats to the stability of project portfolio network (PPN). To maintain PPN stability, a PPN risk propagation model considering risk perception is constructed. First, a PPN is developed to concurrently visualize the risk propagation paths and the risk perception diffusion paths. Second, modifications to the disaster spreading model are made to construct the PPN risk propagation model supporting more realistic simulations. The model is then applied to numerical simulations, yielding risk propagation mitigation measures. The results suggest that risk mitigation is facilitated by increased trust among project managers, moderate differences in initial project manager risk perceptions, a homogeneous PPN topology, and a tendency to allocate recovery resources based on node degree centrality. These findings guide enterprise administrators in risk decision-making, contributing to stable PPN operations.

A novel model is proposed for optimal project portfolio selection (PPS). Different from previous PPS methods that consider the interdependence, interaction, or synergy relationships among projects, this article considers the cascading failure process caused by these relationships. Specifically, we first construct a PP network (PPN) and determine the initial load and capacity of the projects (nodes). Afterward, we simulate the cascading failure process by attacking one or more projects in the PP and distribute the load of the failed projects to nonfailed neighboring projects. The cascading failure process ends until no project fails. Finally, we propose a new indicator to measure and compare each PPN's cascading failure results, based on which the optimal PP is selected. A numerical experiment is conducted to depict the PPS process. The results of this study provide guiding implications for project portfolio management decision-makers, helping them consider PPS problems from a new perspective and make more reasonable decisions.

To facilitate the project portfolio benefits (PPB) management and realize the maximization of the benefits, a PPB measurement model based on the genetic algorithm (GA)-BP neural network group (GA-BPNNG) is proposed in current research. Unlike traditional benefits management approaches, the proposed model takes full cooperation and information sharing advantages of group learning to measure PPB more accurately, which considers both the financial and nonfinancial benefits and synergy benefits generated from the interrelationships among project portfolio components. To ascertain the priority of the PPB measurement model, the GA-BPNNG model is compared with BPNN and GA-BPNN, two common models in the literature. The mean square error of the GA-BPNNG model reduced by 94% and 83%, respectively, when compared to the BPNN and GA-BPNN models. The results suggest that the PPB measurement model performs more effectively. Therefore, it could be concluded that the proposed model has a better nonlinear fitting effect for PPB measurement. This article can support managers, decision-makers, and management in realizing strategy by providing a practical and scalable tool for PPB measurement.