An Adaptive Rollout Algorithm for Clinical Trial Scheduling and Cumulative Resource Allocation under Uncertainty

Xin Fei*, Jürgen Branke, Nalân Gülpınar

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)


Drug development pipeline is composed of projects which are being evaluated in a series of clinical trials with uncertain outcomes. Advancing the pipeline is a race against time, given that most investments can only be recouped by selling the successful projects during the market exclusivity period counted from the filing date. In this study, we introduce a novel optimisation methodology to manage the research and development pipeline by jointly optimising the sequence of clinical trials and the allocation of resources. Our model specifically allows to speed up clinical trials by cumulatively allocating additional resources. A discrete-time stochastic Markov decision process is used to formulate the system dynamics. The state space increases dramatically when scaling up the drug pipeline as a consequence of finding the optimal policy within a reasonable time span is not realistic. We propose an adaptive rollout algorithm that includes three computationally effective innovations. First, the rolling-horizon optimisation framework constructs a base policy that estimates the expected future outcomes after taking a particular action. Second, a statistical racing procedure, which exploits the technique of common random numbers and the empirical Bernstein’s inequality, is proposed to guide the information collection process. Third, we present a hierarchical approach which priorities the actions that makes the best use of available resources so that good actions could be identified at early stage. The numerical results show that the hierarchical and normal statistical racing procedures reduce the number of required simulations, thus providing high-performance results within a reasonable computational time.
Original languageEnglish
JournalEuropean Journal of Operational Research
Publication statusSubmitted - 2020


  • Dynamic Programming
  • Clinical Trial Scheduling
  • Resource Allocation
  • Rollout Algorithm
  • Sampling

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