This preprint introduces an agentic re-optimization framework in which an LLM translates user requests into structured optimization model patches, selects re-optimization tools, and solves updated instances.
The experiments span supply-chain re-optimization and university exam scheduling, showing efficiency gains from primal-based and solver-aware methods while preserving traceable model changes.