Adaptive Robust Optimization with Endogenous Uncertainty and Active Learning in Smart Manufacturing

This award will contribute to the national prosperity by providing rigorous methods to manage uncertainty in complex systems, with a particular focus on manufacturing systems. Uncertainty is pervasive in manufacturing, and safe and efficient operation under uncertainty, as well as effective risk management, is critically important to the nation's manufacturing competitiveness.

This project develops methods to support operational control that explicitly consider endogenous uncertainty, that is, uncertainty that depends directly on decisions. In the manufacturing environment, significant uncertainty is due to the health states of physical assets, and managing these assets effectively constitutes a major challenge. The resulting methods, while validated on manufacturing systems, are expected to be widely applicable to other complex systems, such as energy systems.

This project integrates an adaptive, set-based robust optimization approach incorporating endogenous uncertainty with active learning methods that collect and analyze data on an ongoing basis. Robust optimization presents a tractable approach to advance the state of the art in operational control by addressing various types of endogenous uncertainty.

The project is expected to deepen our understanding of the dependence of uncertainty on decisions, provide a set-based robust optimization perspective on endogenous uncertainty, and introduce a new approach to modeling active learning and optimizing the trade-off between exploration and exploitation. The theoretical advances will be motivated by challenges in smart manufacturing, namely in integrated production and maintenance planning in networked production environments, The PI will include graduate and undergraduate students in the research, and will incorporate optimization methods in capstone design courses in chemical engineering.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.