Pharmaceutical R&D project portfolio selection and scheduling under uncertainty: A robust possibilistic optimization approach

Abstract

Due to the costly and risky nature of research and development (R&D) projects and limited shared resources in pharmaceutical companies, it is essential to simultaneously address the selection and scheduling decisions, and this strongly prevents schedule slippage and poor resource management. Herein, we present a mathematical programming model to periodically select and concurrently schedule an optimal portfolio of R&D projects subject to varying levels of market and technical uncertainty over the desired planning horizon. More specifically, the technical risk of R&D projects is captured using a risk-adjusted net present value (rNPV) approach and their market risk is handled by developing a robust possibilistic programming (RPP) approach which can maximize the mean value of R&D project portfolio and control optimality and feasibility robustness. Given a set of potential R&D projects, limited resources, and a predefined time horizon, the proposed model determines the projects to be executed in each period, decides on the optimal outsourcing policy, and simultaneously concludes the optimal financial resources’ planning. We believe that our model can serve as a strategic decision-making tool to assist pharmaceutical company managers in analyzing various investment scenarios and selecting a balanced portfolio. The significance of the applicability of the developed model is demonstrated via a numerical example and some sensitivity analyses on the data taken from the literature.