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.

The optimization modeling space has long suffered from a fundamental mismatch between how researchers evaluate LLM capabilities and the complexity of real-world industrial problems. While existing benchmarks rely on toy-sized or synthetic examples, actual optimization challenges in logistics, manufacturing, and finance contain orders of magnitude more variables and constraints. This evaluation gap has masked significant performance limitations in LLM-based optimization systems that appear capable on smaller datasets but fail dramatically at scale.

MIPLIB-NL addresses this through a sophisticated reverse-engineering methodology applied to proven real-world optimization models. Rather than synthetically generating problems, researchers recovered structural patterns from existing mixed-integer linear programs, then systematically generated natural-language specifications that remain semantically aligned with original formulations. The iterative validation process involving human experts and independent reconstruction checks ensures benchmark integrity and prevents the common pitfall of synthetic data that diverges from genuine problem characteristics.

The implications extend beyond academic benchmarking. Financial institutions, energy companies, and logistics firms increasingly explore LLM-assisted optimization for strategic decision-making. Accurate evaluation tools reveal where current systems genuinely succeed versus where they merely appear competent on simplified tasks. The reported performance degradation on MIPLIB-NL compared to existing benchmarks suggests production deployments could encounter unexpected failures, highlighting the necessity for rigorous testing infrastructure before real-world adoption.

This work signals growing maturity in AI evaluation standards specifically tailored for domain-critical applications. Future development likely involves expanding MIPLIB-NL's scope and establishing similar benchmarks for other industrial problem classes, creating a foundation where optimization-focused AI tools can be reliably vetted before deployment in high-stakes decision environments.

• →MIPLIB-NL reveals substantial performance gaps in LLMs when evaluated on realistic industrial-scale optimization problems versus toy benchmarks
• →The benchmark preserves genuine mathematical content from 223 real mixed-integer linear programs with thousands to millions of variables
• →Current LLM systems designed for optimization modeling fail dramatically when problem complexity increases by orders of magnitude
• →Rigorous benchmarking standards are essential before deploying LLM-based optimization tools in production finance, logistics, and energy applications
• →Reverse-engineering methodology from proven models provides more reliable evaluation foundation than synthetic problem generation