A Simulation-Optimization Framework for ResearchandDevelopment PipelineManagement

The Research and De®elopment Pipeline management problem has far-reaching economic implications for new-product-de®elopment-dri®en industries, such as pharmaceutical, biotechnology and agrochemical industries. Effecti®e decision-making is required with respect to portfolio selection and project task scheduling in the face of significant uncertainty and an e®er-constrained resource pool. The here-and-now stochastic optimization problem inherent to the management of an R& D Pipeline is described in its most general form, as well as a computing architecture, Sim-Opt, that combines mathematical programming and discrete e®ent system simulation to assess the uncertainty and control the risk present in the pipeline. The R& D Pipeline management problem is ®iewed in Sim-Opt as the control problem of a performance-oriented, resource-constrained, stochastic, discrete-e®ent, dynamic system. The concept of time lines is used to study multiple unique realizations of the controlled e®olution of the discretee®ent pipeline system. Four approaches using ®arious degrees of rigor were in®estigated for the optimization module in Sim-Opt, and their relati®e performance is explored through an industrially moti®ated case study. Methods are presented to efficiently integrate information across the time lines from this framework. This integration of information demonstrated in a case study was used to infer a creati®e operational policy for the corresponding here-and-now stochastic optimization problem.