Heuristic solution approaches to the pharmaceutical R&D pipeline management problem

• Multistage stochastic programmes (SPs) with endogenous uncertainty are considered.
• Two heuristic algorithms are developed to solve clinical trial planning problems.
• A two-stage SP and a knapsack decomposition algorithm are presented.
• The solutions are tight lower bounds for the rigorous optimization formulation.
• The algorithms are orders of magnitude faster than solving the rigorous formulation.

The paper presents two heuristic approaches, a shrinking horizon multiple two-stage stochastic programming (MTSSP) decomposition algorithm and a knapsack decomposition algorithm (KDA), for solving multistage stochastic programmes (MSSPs) with endogenous uncertainty, specifically focusing on pharmaceutical research and development (R&D) pipeline management problem. The MTSSP decomposition algorithm decomposes the problem into a series of two-stage stochastic programmes, which are solved as resources become available. The KDA decomposes the MSSP into a series of knapsack problems, which are created and solved at key decision points on a rolling horizon fashion. Based on the results of the six case studies, both the MTSSP decomposition algorithm and the KDA generate implementable solutions that are within three percent of the rigorous MSSP solution obtained by CPLEX 12.51. Both methods showed several orders of magnitude decrease in the CPU times compared to ones that were required to solve the rigorous MSSP.