This $550,213 Project Grant award, funded by the National Science Foundation (NSF) under the Engineering program (CFDA 47.041), will support research to develop novel machine learning-based optimization techniques for solving complex operational problems. The key products and services to be delivered include: Advancing the field of optimization by creating new learning-enabled cutting plane strategies to improve the performance of commercial mixed integer programming solvers. This will involve theoretical analysis, computational experiments, and model development for applications in power systems, logistics, and healthcare. Incorporating stronger disjunction-based cutting planes to tighten problem formulations and better certify solution quality, while mitigating the computational burden through judicious deployment. Applying the new optimization methods to support logistics operations at local non-profit organizations, coupled with student engagement through engineering senior design projects. The research findings and software implementations will be open-sourced and evaluated on a publicly-available benchmark dataset to yield transparent, actionable insights on effective cutting plane selection strategies. This five-year grant, awarded to the University of Florida, is expected to run from May 1, 2025 to April 30, 2030.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $550.2k | 3/5/25 |