Optimization of R&D project portfolios under endogenous uncertainty

Project portfolio management deals with the dynamic selection of research and development (R&D) projects and determination of resource allocations to these projects over a planning period. Given the uncertainties and resource limitations over the planning period, the objective is to maximize the expected total discounted return or the expectation of some other function for all projects over a long time horizon. We develop a detailed formal description of this problem and the corresponding decision process, and then model it as a multistage stochastic integer program with endogenous uncertainty. Accounting for this endogeneity, we propose an efficient solution approach for the resulting model, which involves the development of a formulation technique that is amenable to scenario decomposition. The proposed solution algorithm also includes an application of the sample average approximation method, where the sample problems are solved through Lagrangian relaxation and a new lower bounding heuristic. The performance of the overall solution procedure is demonstrated using several implementations of the proposed approach.