GOALI: Short-term Scheduling Under Uncertainty: A Robust Optimization Framework

Princeton University, Princeton NJ

Investigators

Abstract

The primary objective of this Grant Opportunity for Academic Liaison with Industry (GOALI) research project is to develop theoretical, algorithmic and computational techniques for addressing the short-term scheduling under uncertainty in chemical processes. The following specific aims will be investigated. In Aim 1, a novel robust optimization framework for addressing uncertainty issues will be studied. In Aim2, the application of the robust optimization framework in Aim 1 to the short-term scheduling of batch and continuous chemical processes which feature multiproduct, multipurpose plants with demands at intermediate due dates and a large number of uncertain parameters will be investigated. The work will result in novel robust optimization models that will form the foundation for addressing the uncertainly in the short-term scheduling of chemical systems. A unique contribution is that this framework can address uncertainly issues for models that contain a large number of uncertain parameters. These robust optimization formulations for mixed-integer linear and nonlinear models have applications in a variety of disciplines, and hence this is a generic component in the proposed work. These theoretical and algorithmic developments will be complemented with industrial case studies and will benefit from our collaboration with the research group at ATOFINA Chemicals Inc. These industrial case studies will be used to validate the robust optimization framework for short-term scheduling chemical systems under uncertainty.