Theories and Computational Algorithms for Optimizing Bilevel Mixed-integer Nonlinear Programs
Abstract
The future vision for the U.S. Air Force includes teamwork and interactions between multiple agents and groups to accomplish military and defense missions in cooperative and-or adversarial environments. Bilevel programs are useful for modeling such interactions, as well as for representing sequential decision-making processes that involve more than one decision maker. Despite research efforts over the past decades on difficult mathematical optimization problems having discrete variables and nonlinear constraints, several major research gaps remain for problems having more than one decision maker- (a) the modeling of bilevel optimization problems with a leader and a follower whose decisions, objectives, and constraints interact with each other in a discrete and nonlinear way; (b) generic reformulation and approximation approaches for recasting bilevel programs to single-level optimization models; and (c) the development of efficient computational algorithms for large-scale bilevel optimization problems based on special and-or general problem structures. This project seeks to conduct research in solving bilevel optimization models with mixed-integer nonlinear programming (MINLP) structures, especially at the lower level. Special modeling approaches will be investigated and computational algorithms for optimizing challenging bilevel optimization problems that arise in diverse applications related to Department of Defense missions will be developed.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 06, 2024
- Source ID
- FA95502310323
Entities
People
- Ruiwei Jiang
Organizations
- Air Force Office of Scientific Research
- Board of Regents of the University of Michigan
- United States Air Force