Multiobjective Long-Term Planning of Biopharmaceutical Manufacturing Facilities K. Lakhdar, † J. Savery, ‡ L. G. Papageorgiou, § and S. S. Farid* ,† Department of Biochemical Engineering, The Advanced Centre for Biochemical Engineering, University College London, Torrington Place, London WC1E 7JE, U.K., BioPharm Services UK, Lancer House, East Street, Chesham, Bucks HP5 1DG, U.K., and Centre for Process Systems Engineering, Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, U.K. Biopharmaceutical companies with large portfolios of clinical and commercial products typically need to allocate production across several multiproduct facilities, including third party contract manufacturers. This poses several capacity planning challenges which are further complicated by the need to satisfy different stakeholders often with conflicting objectives. This work addresses the question of how a biopharmaceutical manufacturer can make better long-term capacity planning decisions given multiple strategic criteria such as cost, risk, customer service level, and capacity utilization targets. A long-term planning model that allows for multiple facilities and accounts for multiple objectives via goal programming is developed. An industrial case study based on a large scale biopharmaceutical manufacturer is used to illustrate the functionality of the model. A single objective model is used to identify how best to use existing capacity so as to maximize profits for different demand scenarios. Mitigating risk due to unforeseen circumstances by including a dual facility constraint is shown to be a reasonable strategy at base case demand levels but unacceptable if demands are 150% higher than expected. The capacity analysis identifies where existing capacity fails to meet demands given the constraints. A multiobjective model is used to demonstrate how key performance measures change given different decision making policies where different weights are assigned to cost, customer service level, and utilization targets. The analysis demonstrates that a high profit can still be achieved while meeting key targets more closely. The sensitivity of the optimal solution to different limits on the targets is illustrated. 1. Introduction An increasing number of large-scale biopharmaceutical companies have a portfolio of commercial products on the market as well as a pipeline of candidates under clinical evaluation. Developing a comprehensive manufacturing strategy to meet anticipated demands for both clinical trial and market material requires careful capacity planning. The launch of successful commercial products has often triggered companies to bridge in-house capacity via strategic partnerships with contract manufacturing organizations (CMO) (Gottschalk, 2005; Kamarck, 2006). Consequently, more effective methods are required to manage and align production across several multi- product facilities, including third party organizations, so as to ensure the availability of sufficient capacity. However, deter- mining capacity needs for biopharmaceutical production is often a difficult process requiring predictions of product doses, market forecasts, production rates (titers, yields) and clinical/technical success rates. The complexity of production planning is further exacerbated by the need to satisfy multiple financial and operational objectives that are often conflicting. Given these decision making challenges, the use of decision support tools to aid the better use of often limited resources is an extremely valuable asset in deciding the most cost-effective plan of action. The value of such tools comes not only from an operational perspective, for example in determining the optimal production and inventory requirements given a set of product demand forecasts, but also in the evaluation of different scenarios to better understand the risks associated with different strategic plans in the face of an uncertain manufacturing environment. Hence, this paper focuses on developing and applying multi- objective optimization models for production planning across a network of multiproduct facilities. Capacity planning and related portfolio management problems have been addressed in the traditional process engineering literature. Mathematical programming formulations have been applied to resource-constrained scheduling for testing of new product development by Jain and Grossmann (1999) and strategic planning for the pharmaceutical industry by Papageor- giou et al. (2001). Gatica et al. (2003) presented a mixed integer linear programming (MILP) formulation for capacity planning under uncertainty for the pharmaceutical industry, and Levis and Papageorgiou (2004) outlined an MILP formulation for long-term, multisite capacity planning under uncertainty in the pharmaceutical industry accompanied by a hierarchical solution algorithm for the model’s efficient solution. Finally, Shah (2004) presented a review of supply-chain optimization in the phar- maceutical industry which encompasses the problem of capacity planning. * To whom correspondence should be addressed. E-mail: s.farid@ucl.ac.uk. † The Advanced Centre for Biochemical Engineering, University College London. ‡ BioPharm Services UK. § Department of Chemical Engineering, University College London. 1383 Biotechnol. Prog. 2007, 23, 1383-1393 10.1021/bp0701362 CCC: $37.00 © 2007 American Chemical Society and American Institute of Chemical Engineers Published on Web 10/09/2007