Agentic AI-Assisted Modeling for Production Scheduling: Assessment in Constraint Programming
Developing optimization models for production scheduling requires substantial expert effort. Research on large language models (LLMs) has followed two directions: specialized approaches for automated modeling, mostly for mixed-integer linear programming, which often rely on dedicated training or problem-specific architectures that limit industrial deployment; and agentic artificial intelligence for operational decision support, which generally assumes that the optimization model already exists. This study bridges both directions by assessing whether general-purpose LLMs, orchestrated as agents without task-specific training, can formulate and implement constraint programming models from n...
| Authors | Ángel Sánchez-Fernández, Javier Pernas-Álvarez, Diego Crespo-Pereira |
|---|---|
| Primary category | cs.AI |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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