Constructing Industrial-Scale Optimization Modeling Benchmark
Zhong Li, Hongliang Lu, Tao Wei, Wenyu Liu, Yuxuan Chen, Yuan Lan, Fan Zhang, Zaiwen Wen

TL;DR
This paper introduces MIPLIB-NL, a large-scale, realistic benchmark for evaluating natural-language to optimization modeling systems, revealing significant challenges in industrial-scale problem translation.
Contribution
It presents a novel reverse construction pipeline to create a benchmark linking natural language specifications with real optimization models, addressing a key evaluation gap.
Findings
Existing systems perform poorly on MIPLIB-NL, exposing limitations not visible in toy benchmarks.
The benchmark includes 223 validated instances derived from real mixed-integer linear programs.
Experiments highlight the need for improved natural-language understanding in industrial optimization.
Abstract
Optimization modeling underpins decision-making in logistics, manufacturing, energy, and finance, yet translating natural-language requirements into correct optimization formulations and solver-executable code remains labor-intensive. Although large language models (LLMs) have been explored for this task, evaluation is still dominated by toy-sized or synthetic benchmarks, masking the difficulty of industrial problems with -- (or more) variables and constraints. A key bottleneck is the lack of benchmarks that align natural-language specifications with reference formulations/solver code grounded in real optimization models. To fill in this gap, we introduce MIPLIB-NL, built via a structure-aware reverse construction methodology from real mixed-integer linear programs in MIPLIB~2017. Our pipeline (i) recovers compact, reusable model structure from flat solver formulations,…
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Taxonomy
TopicsConstraint Satisfaction and Optimization · Formal Methods in Verification · Advanced Multi-Objective Optimization Algorithms
