Using mean-Gini and stochastic dominance to choose project portfolios with parameter uncertainty

Although a variety of models have been studied for project portfolio selection, many organizations still struggle to choose a potentially diverse range of projects while ensuring the most beneficial results. The use of the mean-Gini framework and stochastic dominance to select portfolios of research and development (R&D) projects has been gaining attention in the literature despite the fact that such approaches do not consider uncertainty regarding the projects' parameters. This article discusses, with relation to project portfolio selection through a mean-Gini approach and stochastic dominance, the impact of uncertainty on project parameters. In the process, Monte Carlo simulation is considered in evaluating the impact of parametric uncertainty on project selection. The results show that the influence of uncertainty is significant enough to mislead managers. A more robust selection policy using the mean-Gini approach and Monte Carlo simulation is proposed.