Optimization Under Uncertainty for the Design and Planning of Process Systems
Ignacio E. Grossmann, Dept of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213
This presentation will give an overview on major formulations and strategies for incorporating effects of uncertainties in the design, planning and scheduling of process systems. We first present general approaches and formulations that rely on deterministic and probabilistic representations. The former lead to problems related to flexibility and robustness, while the latter lead to problems that either work with scenarios in one or several decision stages, or with probabilities for meeting constraints. We illustarte the application of these techniques on several applications outside the pharmaceutical domain that include optimal design of heat exchanger networks, optimal batch scheduling with uncertain demands, optimal design of offshore platforms, and optimal planning of sustainable supply chains with uncertain emissions. Finally, we close with a specific application in the pharmaceutical industry that deals with simultaneous optimization of new product development and design of batch facilities. As will be shown throughout these examples the handling of uncertainty gives rise to very challenging optimization problems, which however, provide important benefits compared to ad-hoc approaches that use deterministic methods.