This $172,000 Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports the development of machine learning models and optimization algorithms for improving disaster response logistics. The primary objectives of the project are to: Research existing machine learning models for clustering regions and develop an enhanced K-means++ clustering model with improved time complexity. Develop a novel dual-determinant clustering algorithm that considers regional capacity and distance from disaster response depots to optimize post-disaster logistics. Create real-time algorithms and a decision support system for dynamically routing disaster response logistics during disease outbreaks, including integrating locations with uncertain demand. The award recipient is the CSU Fullerton Auxiliary Services Corporation, a non-profit sponsored programs office that supports research and education initiatives at California State University, Fullerton, a Hispanic-Serving Institution. The project aims to advance theoretical and applied knowledge in combinatorial optimization to enhance the scalability of solutions for computationally complex real-world problems, with a focus on improving disaster response capabilities, especially in low- and middle-income countries. The award period is from September 1, 2024 to August 31, 2026.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $172.0k | 8/3/24 |