Type-Aware Retrieval-Augmented Generation with Dependency Closure for Solver-Executable Industrial Optimization Modeling

Researchers developed a type-aware retrieval-augmented generation (RAG) method that translates natural language requirements into solver-executable optimization code for industrial applications. The method uses a typed knowledge base and dependency closure to ensure code executability, successfully validated on battery production optimization and job scheduling tasks where conventional RAG approaches failed.