A Study on Project Portfolio Models with Skewness Risk and Staffing

Abstract

When it comes to business operation, the institutions have to choose some appropriate projects from numerous projects to invest. To this end, they should consider to establish a project portfolio to make decisions. When building such a portfolio, the project selection and the staff assignment are the most essential parts, which greatly affect the profit of project portfolios. As for the project selection, market returns tend to be asymmetric and investors are often concerned about the skewness risk which is ignored by the traditional project portfolio. Meanwhile, as for the staff assignment, the institutional investors aim at achieving the highest returns by adopting a proper assignment of project managers. In addition, since the exact possibility distributions of uncertain parameters in practical project portfolio problems are often unavailable, we adopt variable parametric credibility measure to characterize uncertain model parameters. In view of these problems, this article proposes a project portfolio model with skewness risk constraints and a project portfolio model with staffing based on credibility measure theory and fuzzy theory in uncertain circumstances. Our two models are associated with risk-free assets so that the remaining funds can be utilized effectively. Finally, we use genetic algorithms to solve our proposed models and present some numerical examples to demonstrate the effectiveness of the proposed models.