Simulation-Optimization Framework for Stochastic Optimization of R&D Pipeline Management

( ) The simulation-based optimization framework Sim-Opt uses a twin-loop computational architecture, which combines mathematical programming and discrete e®ent simulation, to address this problem. This article extends our earlier work to present methods ( for integrating information from the inner loop Sim-Opt time lines, reacti®e adjust) ( ) ment and using it in the outer risk-control loop Stochastic Optimization loop to obtain statistically significant impro®ements in the solutions to the underlying stochastic optimization problem. Two classes of information can be obtained from the inner loop time lines: the first pertaining to portfolio selection and the second resource crowding associated with the chosen operation policy. Methods presented quantify the information on these two classes, and a three-step heuristic incorporates this information in the outer risk-control loop to dri®e the system toward impro®ing solutions with respect to ( ) the mean net present ®alue NPV of the portfolio and the probability of deli®ering a positi®e NPV. This method was used on a pharmaceutical product de®elopment case study, consisting of 11 projects, 154 acti®ities, 14 resource types and a 20-year planning horizon with respect to patent expiration. Basic algorithm engineering efforts are also described to significantly impro®e the performance of formulation generation, the generation of a heuristic lower bound and the identification of cut families to effecti®ely apply branch-and-cut methods.