Requirement-Aligned LLM Modeling for Hot Rolling Scheduling in Steel Manufacturing

Steel manufacturing's high energy use and carbon emissions make effective production scheduling—particularly hot rolling scheduling—important for efficiency and sustainability. However, complex, dynamic, and highly specific requirements of steel production limit the flexibility of conventional optimization methods, leaving scheduling dependent on local human expertise. Large language models (LLMs) can construct and adapt models for the hot rolling scheduling problem (HRSP) from natural-language requirements, but incomplete or inconsistent formulations may be infeasible or yield schedules that violate the target production constraints. To address these limitations, we propose a requirement-aligned LLM-based framework for constructing and solving multi-objective HRSP models from natural-language descriptions and instance data. A knowledge base organizes expert formulations into reusable decision, constraint, and objective components with production semantics and dependency records. Component Selection matches components to target requirements through semantic comparison, evidence verification, and coverage checks that recover omissions. Component Composition aligns decision definitions and dependencies to assemble the selected components into a consistent constraint system and objective vector. Model and Solution Verification checks component dependencies and data references before solving, then independently validates target feasibility, objective values, and Pareto non-dominance. The framework achieves F1 scores of 0.952 for component selection and 0.966 for adaptation, with all returned schedules passing independent checks of the target constraints. Our code is available at https://anonymous.4open.science/r/llm4hrsp