A multistage stochastic programming approach for capital budgeting problems under uncertainty

This paper addresses the capital budgeting problem under uncertainty. In particular, we propose a multistagestochastic programming model aimed at selecting and managing a project portfolio. The dynamicuncertain evolution of each project value is modelled by a scenario tree over the planning horizon. Themodel allows the decision maker to revise decisions by decommitting from a given project if it shows anegative performance. Risk is explicitly assessed by defining a mean-risk objective function, where theconditional value at risk is used. A customized branch-and-bound method is also introduced for solvingthe proposed model. Extensive computational experiments have been carried out to validate the modeleffectiveness, also in comparison with other possible benchmark policies. The numerical results collectedby solving randomly generated instances with the proposed branch-and-bound approach seems tobe encouraging.