Mean-variance capital budgeting model for R&D project selection

This paper discusses optimal selection of R&D projects under uncertainty. The stochastic optimization models for R&D project selection are not almost studied except Ringuest et al. We formulate two kinds of mean–variance capital budgeting models for R&D project selection. One is the standard model with zero–one variables, in which the value is zero if the project is rejected and one if the project is accepted. The other is the practical model with inequality constraints with zero–one variables, in which the value is zero if the project is rejected and more than lower limit if the project is accepted. Multiple scores evaluated by multiple specialists are used as future scenarios of projects. We run these models with hypothetical data and examine the results.