Progressive Context Enrichment for LLM-Based MILP Formulation in Open-Pit Mine Production Scheduling
This paper proposes a progressive context-enrichment framework that enhances the reliability of Large Language Models in generating Mixed Integer Linear Programming formulations for open-pit mine production scheduling by systematically augmenting prompts with simulator data and code annotations, revealing that while simulator code improves objective accuracy, annotated code is superior for ensuring constraint feasibility.