Capacity planning for batch and perfusion bioprocesses across multiple biopharmaceutical facilities |
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| Authors: | Cyrus C. Siganporia Soumitra Ghosh Thomas Daszkowski Lazaros G. Papageorgiou Suzanne S. Farid |
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| Affiliation: | 1. Dept. of Biochemical Engineering, University College London, London, U.K.;2. Biotechnology Controlling, Bayer Healthcare, Berkeley, California, USA;3. Process Development and Optimisation, Bayer AG, Leverkusen, Germany;4. Dept. of Chemical Engineering, Centre for Process Systems Engineering, University College London, London WC1E 7JE, U.K. |
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| Abstract: | Production planning for biopharmaceutical portfolios becomes more complex when products switch between fed‐batch and continuous perfusion culture processes. This article describes the development of a discrete‐time mixed integer linear programming (MILP) model to optimize capacity plans for multiple biopharmaceutical products, with either batch or perfusion bioprocesses, across multiple facilities to meet quarterly demands. The model comprised specific features to account for products with fed‐batch or perfusion culture processes such as sequence‐dependent changeover times, continuous culture constraints, and decoupled upstream and downstream operations that permit independent scheduling of each. Strategic inventory levels were accounted for by applying cost penalties when they were not met. A rolling time horizon methodology was utilized in conjunction with the MILP model and was shown to obtain solutions with greater optimality in less computational time than the full‐scale model. The model was applied to an industrial case study to illustrate how the framework aids decisions regarding outsourcing capacity to third party manufacturers or building new facilities. The impact of variations on key parameters such as demand or titres on the optimal production plans and costs was captured. The analysis identified the critical ratio of in‐house to contract manufacturing organization (CMO) manufacturing costs that led the optimization results to favor building a future facility over using a CMO. The tool predicted that if titres were higher than expected then the optimal solution would allocate more production to in‐house facilities, where manufacturing costs were lower. Utilization graphs indicated when capacity expansion should be considered. © 2013 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 30:594–606, 2014 |
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| Keywords: | capacity planning scheduling business decision‐making mixed integer linear programming rolling time horizon |
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