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Sundaramoorthy A.,Process Systems Engineering Laboratory | Evans J.M.B.,Massachusetts Institute of Technology | Barton P.I.,Process Systems Engineering Laboratory
Industrial and Engineering Chemistry Research | Year: 2012

Unlike traditional batch-based pharmaceutical manufacturing, where the active pharmaceutical ingredient (API) and the final drug product are often produced in different facilities at different locations, novel continuous pharmaceutical manufacturing strategies enable the production of both the API and the final drug product in the same integrated facility. The capacities of such integrated continuous facilities must be determined for potential products in the face of clinical trials uncertainty. Given a portfolio consisting of potential products in the development stage, the goal of capacity planning is to ensure the availability of enough production capacity to meet the projected demands of products, which vary from the launch to the peak-demand periods. To address this problem, we propose a multiscenario, multiperiod, mixed-integer linear programming (MILP) formulation that takes into account uncertainty in the outcome of clinical trials. We illustrate the proposed framework using several examples. The exponential increase in problem size with the number of products motivates us to develop an efficient solution method, which is discussed in Part 2 of this paper. © 2012 American Chemical Society.


Sundaramoorthy A.,Process Systems Engineering Laboratory | Li X.,Process Systems Engineering Laboratory | Li X.,Queen's University | Evans J.M.B.,Massachusetts Institute of Technology | Barton P.I.,Process Systems Engineering Laboratory
Industrial and Engineering Chemistry Research | Year: 2012

In Part 1 of this paper, we presented a scenario-based multiperiod mixed-integer linear programming (MILP) formulation for a capacity planning problem in continuous pharmaceutical manufacturing under clinical trials uncertainty. The number of scenarios and, thus, the formulation size grows exponentially with the number of products. The model size easily becomes intractable for conventional algorithms for more than 8 products. However, industrial-scale problems often involve 10 or more products, and thus a scalable solution algorithm is essential to solve such large-scale problems in reasonable times. In this part of the paper, we develop a rigorous decomposition strategy that exploits the underlying problem structure. We demonstrate the effectiveness of the proposed algorithm using several examples containing up to 16 potential products and over 65 000 scenarios. With the proposed decomposition algorithm, the solution time scales linearly with the number of scenarios, whereby a 16-product example with over 65 million binary variables, nearly 240 million continuous variables, and over 250 million constraints was solved in less than 6 h of solver time. © 2012 American Chemical Society.

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