Complexity Science mini-project:
Integrated Capacity Planning and Portfolio Selection for
Biopharmaceutical Production
Richard Allmendinger (UCL), Juergen Branke (Warwick Business School), Suzy Farid (UCL)
Background: Development and production of biopharmaceutical drugs is expensive and risky.
Efficient capacity planning and portfolio selection are crucial, but at the same time become ever
more complex as the number of potential drug candidates increases and their manufacturing
processes vary. Furthermore, decisions are interdependent, i.e., the decision about which drugs to
develop depends on the available capacities and resource needs of the other drugs, and vice versa.
And because of the uncertainties involved in drug development and testing, plans have to be
continually updated as new test results become available [1]. In a research project funded by EPSRC
and in collaboration between UCL and Warwick, we develop novel decision-support tools for such
scenarios.
Mini-project: The mini-project will develop an evolutionary algorithm and/or approximate dynamic
programming in combination with simulation to optimize the selection of drugs to be developed as
well as the schedule. The project thus consists of three parts: Modelling (which aspects/constraints
of the problem are most relevant and need to be modelled accurately), methodology development
(designing and implementing the optimization algorithm) and testing the developed algorithms on a
given dataset.
PhD prospect: The mini-project is only a first step in developing a tool to support biopharmaceutical
companies in making selection and planning decisions in this complex environment. Promising
techniques to be used are evolutionary algorithms or approximate dynamic programming, and a
sound comparison of these techniques would have relevance also for other applications. Notable
extensions of the problem scenario include the consideration of multiple objectives [2,3], the
alternative of outsourcing certain production steps to third parties [4], or the consideration of
changeover times [1]. Because a simulation-based approach to evaluate solutions may become
computationally very expensive as the problem size grows, one may have to integrate surrogate
models [6], which opens an entire new field of interesting research questions.
EPSRC funding for a continuation of the project as PhD student may be available.
Deliverables:
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Literature survey on pharmaceutical project selection
Simulation model for a simple biopharmaceutical drug development and production process
Either an evolutionary algorithm or an approximate dynamic programming model to
optimize the combined drug selection and production planning problem
Student’s requirements:
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Programming skills
Exemplary bibliography
[1] J. Yaochu and J. Branke. Evolutionary optimization in uncertain environments-a survey. IEEE
Transactions on Evolutionary Computation 9(3): 303-317 (2005)
[2] J. Branke, K. Deb, K. Miettinen, and R. Slowinski (Eds.). Multiobjective optimization: Interactive
and evolutionary approaches (Vol. 5252), Springer (2008)
[3] E. George, N. J. Titchener-Hooker, and S. S. Farid. A multi-criteria decision-making framework for
the selection of strategies for acquiring biopharmaceutical manufacturing capacity. Computers &
Chemical Engineering 31(8): 889-901 (2007)
[4] C. Siganporia, S. Ghosh, T. Daszkowski, L. G. Papageorgiou, and S. S. Farid. Production planning of
batch and semi-continuous bioprocesses across multiple biopharmaceutical facilities. Computer
Aided Chemical Engineering 30:377-381 (2012)
[5] E. George and S. S. Farid. Stochastic Combinatorial Optimization Approach to Biopharmaceutical
Portfolio Management. Industrial and Engineering Chemistry Research 47(22): 8762-8774 (2008)
[6] J. Knowles and H. Nakayama. Meta-modeling in multiobjective optimization. Multiobjective
Optimization. Springer Berlin Heidelberg, pp 245-284 (2008).