Uncertain Public R&D Project Portfolio Selection Considering Sectoral Balancing and Project Failure

In order to promote scientific and technological innovation and sustainable development, public funding agencies select and fund a large number of R&D projects every year. To guarantee the performance of the resulting project portfolio and the government's investment benefits, the decision maker needs to select appropriate projects and determine a reasonable funding amount for each selected project. In the process of project selection, it is necessary to consider the balance of funding allocated to different scientific sectors as well as the failure probability of the projects in future execution, so that the expected performance of the project portfolio is maximized as much as possible. In view of this, we propose and study the uncertain public R&D project portfolio selection problem considering sectoral balancing and project failure. We formulate a stochastic programming model for the problem to support the portfolio decisions of the funding agencies. We also transform the model into an equivalent deterministic second-order cone programming model that can be directly solved by exact solvers. We generate datasets reflecting different scenarios through simulation and perform computational experiments to validate our model. The impacts of various factors (i.e., the number of project proposals, project failure probability, the upper limit of the budget allocated to each project, and the decision maker's tolerance for project failure) on the project portfolio performance are analyzed.

Automated summary

In this study, we propose a stochastic programming model for the uncertain public R&D project portfolio selection problem and transform it into an equivalent deterministic second-order cone programming model. Through simulation and computational experiments, we analyze the impacts of various factors on the project portfolio performance.