Multi-Objective Pharmaceutical Portfolio Optimization under Uncertainty of Cost and Return

Abstract

This paper presents the study of multi-objective optimization of a pharmaceutical portfolio when both cost and return values are uncertain. Decision makers in the pharmaceutical industry encounter several challenges in deciding the optimal selection of drug projects for their portfolio since they have to consider several key aspects such as a long product-development process split into multiple phases, high cost and low probability of success. Additionally, the optimization often involves more than a single objective (goal) with a non-deterministic nature. The aim of the study is to develop a stochastic multi-objective approach in the frame of chance-constrained goal programming. The application of the results of this study allows pharmaceutical decision makers to handle two goals simultaneously, where one objective is to achieve a target return and another is to keep the cost within a finite annual budget. Finally, the numerical results for portfolio optimization are presented and discussed.

Replenishing the pipeline: A quantitative approach to optimising the sourcing of new projects

“…Farid et al 7 use a chance‐constrained method, accounting for cost uncertainties. In Reference 11, a multi‐objective method is used while accounting for uncertainty in both cost and revenue. Some authors have applied real option valuations as the basis for optimising pharmaceutical R&D portfolios, for example Reference 12.…”