The R&D pipeline management problem has far-reaching economic implications for new-product-development driven industries, such as pharmaceutical, biotechnology, and agrochemical industries. Effective decision-making is required with respect to portfolio selection and project task scheduling in the face of significant uncertainty and an ever-constrained resource pool. In this paper, the here-and-now stochastic optimization problem inherent to the management of an R&D pipeline is described in its most general form. Subsequently, a computing architecture, Sim-Opt, is presented that combines mathematical programming and discrete event system simulation to assess the uncertainty and control the risk present in the pipeline. The concept of timelines, that studies multiple unique realizations of the controlled evolution of the discrete-event pipeline system, is introduced. Three different implementations of the decision-making module in Sim-Opt have been described and studied through an example case study.