Increasing pressures exist in the biopharmaceutical sector for the design of flexible and cost-effective multi-product facilities that can cope with diverse drug candidate characteristics and process variations. This is due to the high risks of clinical failure as well as pressures to contain costs and enhance capacity utilisation. To address this problem, a genetic algorithm is created that can handle multiple decisions, trade-offs and uncertainties simultaneously to design multi-product facilities, where the decision variables represent choices at different levels  (facility, product, sequence, unit operation). An industrially-relevant case study is presented that addresses the design of commercial manufacturing facilities for the production of monoclonal antibodies. The most cost-effective purification sequences and equipment sizing strategies that meet demand and purity targets are identified for each product and methods to visualise trade-offs in the set of optimal solutions with similar cost values are provided so as to enhance the decision making process.

Optimization of multiproduct antibody facility designs: A genetic algorithm approach

243rd National Meeting (2012)

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