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Title: Strategic biopharmaceutical production planning for batch and perfusion processes
Author: Siganporia, C. C.
ISNI:       0000 0004 8497 7037
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2016
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Capacity planning for multiple biopharmaceutical therapeutics across a large network of manufacturing facilities, including contract manufacturers, is a complex task. Production planning is further complicated by portfolios of products requiring different modes of manufacture: batch and continuous. Capacity planning decisions each have their own costs and risks which must be carefully considered when determining manufacturing schedules. Hence, this work describes a framework which can assimilate various input data and provide intelligent capacity planning solutions. First of all, a mathematical model was created with the objective of minimising total cost. Various challenges surrounding the biomanufacturing of both perfusion and fed-batch products were solved. Sequence-dependent changeover times and full decoupling between upstream and downstream production suites were incorporated into the mixed integer linear program, which was used on an industrial case study to determine optimal manufacturing schedules and capital expenditure requirements. The effect of varying demands and fermentation titres was investigated via scenario analysis. To improve computational performance of the model, a rolling time horizon was introduced, and was shown to not only improve performance but also solution quality. The performance of the model was then improved via appropriate reformulations which consider the state task network (STN) topology of the problem domain. Two industrial case studies were used to demonstrate the merits of using the new formulation, and results showed that the STN improved performance in all test cases, and even performed better than the rolling time horizon approach from the previous model in one test case. Various strategic options regarding capacity expansion were analysed, in addition to an illustration of how the framework could be used to de-bottleneck existing capacity issues. Finally, a multi-objective component is added to the model, enabling the consideration of strategic multi-criteria decision making. The ε-constraint method was shown to be the superior multi-objective technique, and was used to demonstrate how uncertain input parameters could affect the different objectives and capacity plans in question.
Supervisor: Farid, S. ; Papageorgiou, L. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available