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• The proposed type-aware RAG method constructs a domain-specific typed knowledge base by parsing heterogeneous sources into typed units and encoding mathematical dependencies in a knowledge graph.method 90%
• The type-aware RAG method computes a minimal dependency-closed context via dependency propagation over the knowledge graph to ensure solver-executable code.method 90%
• In a demand response optimization case in battery production, the method generates an executable model incorporating demand-response incentives and load-reduction constraints, achieving peak shaving while preserving profitability.result 85%
• In a flexible job shop scheduling case, the method consistently produces compilable models that reach known optimal solutions, demonstrating robust cross-domain generalization.result 85%
• Enforcing type-aware dependency closure is essential for avoiding structural hallucinations and ensuring executability in generated models.technical 90%
• Conventional RAG baselines fail to generate executable models in the demand response optimization case.result 90%
• Conventional RAG baselines fail entirely in the flexible job shop scheduling case.result 90%
• Accurate extraction and parsing from heterogeneous sources may limit the universality of the proposed method.limitation 70%
• The proposed type-aware retrieval-augmented generation method generates solver-executable optimization models from natural language instructions, unlike conventional RAG baselines which fail.result 90%
• The method constructs a domain-specific typed knowledge base by parsing heterogeneous sources like academic papers and solver code into typed units and encoding their mathematical dependencies in a knowledge graph.method 90%
• The method computes a minimal dependency-closed context via dependency propagation over the knowledge graph to ensure executability.technical 85%
• The method demonstrates robust cross-domain generalization by producing compilable models that reach known optimal solutions in flexible job shop scheduling.result 85%

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