#optimization-modeling News & Analysis

4 articles tagged with #optimization-modeling. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AIBullisharXiv – CS AI · Mar 47/103

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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.

AINeutralarXiv – CS AI · 4d ago6/10

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Grammar-Aware Literate Generative Mathematical Programming with Compiler-in-the-Loop

Researchers introduce SyntAGM, an AI system that generates mathematical optimization models in readable algebraic language rather than general-purpose code. The system uses a compiler-in-the-loop approach with iterative feedback to improve model accuracy, achieving better cost-quality trade-offs than existing language model baselines.

AINeutralarXiv – CS AI · 5d ago6/10

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OR-Space: A Full-Lifecycle Workspace Benchmark for Industrial Optimization Agents

Researchers introduce OR-Space, a comprehensive benchmark for evaluating large language model agents in industrial operations research workflows. Unlike existing benchmarks that focus on single-stage problem translation, OR-Space tests agents across persistent multi-artifact workspaces with three task modes—building optimization models, revising them under changing requirements, and explaining solutions—to assess real-world reliability and practical readiness.

AINeutralarXiv – CS AI · 6d ago6/10

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Constructing Industrial-Scale Optimization Modeling Benchmark

Researchers introduce MIPLIB-NL, a benchmark dataset of 223 industrial-scale optimization problems derived from real mixed-integer linear programs. The benchmark bridges natural-language problem descriptions with executable solver code, addressing a critical gap in evaluating large language models on realistic optimization tasks with thousands to millions of variables and constraints.