BAYESIAN RISK-AVERSE AND DISTRIBUTIONALLY-ROBUST APPROACHES TO DATA-DRIVEN STOCHASTIC OPTIMIZATION
Abstract
The research is concerned with the study of basic questions aimed at challenges in logistics and planning for the Air Force of the future, including path planning, target tracking, and resource allocation. Many of these problems can be modeled and solved in the framework of stochastic optimization either in the static or dynamic setting. In practice, the distribution of the randomness in the system (such as target movements, random demands for resource) is never known exactly, but data about the randomness can be collected sequentially over time. With the consideration of distributional uncertainty and streaming data, the project is to develop high-performance approaches to data-driven stochastic optimization.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 07, 2023
- Source ID
- FA95502210244
Entities
People
- Enlu Zhou
Organizations
- Air Force Office of Scientific Research
- Georgia Tech Research Corporation
- United States Air Force