Decision Making for Large-Scale Problems Under Uncertainty and Conflict

Author

• Benjamin J. Hamlin, Clemson UniversityFollow

Date of Award

5-2026

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Mathematical Sciences

Committee Chair/Advisor

Cheng Guo

Committee Member

Margaret M Wiecek

Committee Member

Nathan Adelgren

Committee Member

Hao Hu

Committee Member

Matthew Saltzman

Abstract

Large-scale decision-making problems appear in many areas including long-range forecasting such as energy generation forecasting. Many such problems are subject to conflicting objectives and uncertain data, and can be modeled as linear optimization problems. We study novel theoretical results and algorithms for large-scale linear decision problems under conflict and uncertainty. First, we propose a parametric Benders decomposition algorithm for solving large-scale linear optimization problems with multiple objectives or deterministically uncertain objectives. Second, we extend the parametric Benders decomposition to a multi-stage setting, developing a parametric stochastic dual dynamic programming algorithm, which enables decision-making when conflicts and uncertainty have planning impacts on multiple future stages. To improve the practicality of the algorithm, we explore accelerating the computation through randomization and algorithmic enhancements to compensate for solver deficiencies. We implement the parametric stochastic dual dynamic programming algorithm along with the accelerations and enhancements discussed on a real-world hydrothermal energy generation problem and discuss the numerical results.

Recommended Citation

Hamlin, Benjamin J., "Decision Making for Large-Scale Problems Under Uncertainty and Conflict" (2026). All Dissertations. 4262.
https://open.clemson.edu/all_dissertations/4262

Author ORCID Identifier

0009-0005-4264-2049