An Integer Programming Approach for Evaluating R&D Funding Decisions With Optimal Budget Allocations

The use of real options techniques for research and development (R&D) project selection to mitigate the uncertainties has been shown to increase overall project value. While recent approaches employing stochastic dynamic programs (SDP) produce optimal solutions for many applications, this approach does not easily accommodate the inclusion of an optimal a priori budget allocation or side constraints, since their formulations are scenario specific. We formulate an integer program (IP) whose solution is equivalent to previous SDP real options models but facilitates the incorporation of these additional features and may be solved using commercial IP solvers. This IP formulation can solve what would otherwise be a nested two-level problem where the lower level problem is an SDP. We then compare the performance of the IP to that received by the SDP using a case study from the literature.