Capacity Planning for Continuous Pharmaceutical Manufacturing Facilities

In this work, we develop an integrated optimization under uncertainty framework to address the problem of capacity planning in pharmaceutical supply chains that employ novel integrated continuous production schemes. Since the outcomes of clinical trials are uncertain, the problem naturally leads to a stochastic programming problem, which is formulated as a scenario-based multi-period mixed-integer linear programming (MILP) model. The number of scenarios grows exponentially with the number of potential products, resulting in large-scale MILP models that are intractable with standard solvers. We propose a novel solution method to solve the above industrial-scale problems that contain up to 10 potential products and nearly 60,000 scenarios.