Laboratory for Interactive Optimization and Learning

(placeholder)
(placeholder)

Physical Flow and other

Industrial Challenges

Improving service-levels and performance by leveraging data

At the core of many societal challenges, particularly in view of sustainability and efficiency, there are hard optimization problems. While these underlying problems can be solved for smaller setups using optimization methods, realistic problem sizes are still prohibitive. In today's fast-paced and intertwined economies we additionally face the challenge of addressing real-time aspects; otherwise the problem changes faster than it was solved.

Physical Flow and other Industrial Challenges.


At the core of many societal challenges, particularly in view of sustainability and efficiency, there are hard optimization problems. While these underlying problems can be solved for smaller setups using optimization methods, realistic problem sizes are still prohibitive. Apart from the immediate challenges arising from sheer size, in today's fast-paced and intertwined economies we additionally face the challenge of addressing real-time aspects; otherwise, the obtained solutions might not apply anymore: the problem changed faster than it was solved. This EAGER addresses specific optimization problems arising from societal challenges and will motivate the study of the underlying optimization problems. For this, new methods at the intersection of continuous and discrete optimization as well as machine learning and randomization will be developed.


NSF-funded research project

CCF-1415496


PI Prasad Tetali

Co-PI Henrik Christensen

Co-PI George Nemhauser

Co-PI Sebastian Pokutta




(placeholder)