EvoOpt-LLM: Evolving industrial optimization models with large language models

• URL: http://arxiv.org/abs/2602.01082v1
• Date: Sun, 01 Feb 2026 07:58:55 GMT
• Title: EvoOpt-LLM: Evolving industrial optimization models with large language models
• Abstract summary: EvoOpt-LLM is a unified LLM-based framework supporting the full lifecycle of industrial optimization modeling.<n>It achieves a generation rate of 91% and an executability rate of 65.9% with only 3,000 training samples, with critical performance gains emerging under 1,500 samples.<n>The constraint injection module reliably augments existing MILP models while preserving original objectives, and the variable pruning module enhances computational efficiency, achieving an F1 score of 0.56 on medium-sized LP models with only 400 samples.
• Score: 43.621054283138115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optimization modeling via mixed-integer linear programming (MILP) is fundamental to industrial planning and scheduling, yet translating natural-language requirements into solver-executable models and maintaining them under evolving business rules remains highly expertise-intensive. While large language models (LLMs) offer promising avenues for automation, existing methods often suffer from low data efficiency, limited solver-level validity, and poor scalability to industrial-scale problems. To address these challenges, we present EvoOpt-LLM, a unified LLM-based framework supporting the full lifecycle of industrial optimization modeling, including automated model construction, dynamic business-constraint injection, and end-to-end variable pruning. Built on a 7B-parameter LLM and adapted via parameter-efficient LoRA fine-tuning, EvoOpt-LLM achieves a generation rate of 91% and an executability rate of 65.9% with only 3,000 training samples, with critical performance gains emerging under 1,500 samples. The constraint injection module reliably augments existing MILP models while preserving original objectives, and the variable pruning module enhances computational efficiency, achieving an F1 score of ~0.56 on medium-sized LP models with only 400 samples. EvoOpt-LLM demonstrates a practical, data-efficient approach to industrial optimization modeling, reducing reliance on expert intervention while improving adaptability and solver efficiency.

Related papers

• little m: An AI Agent for Industrial Process Optimization [6.819597984555337]
  We introduce little m, an AI agent designed to assist the formulation of industrial process control models.<n>For systematic evaluation, we introduce the Industrial Process Control Benchmark (IPC-Bench), a novel dataset of 50 canonical scenarios.<n>Little m substantially outperforms state-of-the-art LLMs, generating semantically correct models.
  arXiv  Detail & Related papers  (2026-09-15T06:01:18Z)
• FormuEvo: LLM-Guided Evolution for Discovering Solver-Efficient Mixed-Integer Programming Formulations [66.32852384262038]
  Mixed-integer programming (MIP) lies at the core of operations research and industrial optimization.<n>We propose FormuEvo, an evolutionary framework for automated discovery of solver-efficient MIP formulations.<n>FormuEvo frames MIP formulation design as evolutionary optimization over the symbolic space of MIP formulations.
  arXiv  Detail & Related papers  (2026-08-24T15:01:28Z)
• AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems [54.593031581486116]
  We present AutoOR, a scalable synthetic data generation and reinforcement learning pipeline that trains LLMs to autoformalize optimization problems specified in natural language.<n>AutoOR generates verified training data from standard optimization forms and uses solver execution feedback as the reward signal for RL post-training.<n>We believe that methods such as AutoOR can significantly accelerate industrial decision-making with AI.
  arXiv  Detail & Related papers  (2026-04-18T03:24:54Z)
• MM-HELIX: Boosting Multimodal Long-Chain Reflective Reasoning with Holistic Platform and Adaptive Hybrid Policy Optimization [103.74675519953898]
  Long-chain reflective reasoning is a prerequisite for solving complex real-world problems.<n>We build a benchmark consisting 1,260 samples of 42 challenging synthetic tasks.<n>We generate post-training data and explore learning paradigms for exploiting such data.
  arXiv  Detail & Related papers  (2025-10-09T17:53:58Z)
• CALM Before the STORM: Unlocking Native Reasoning for Optimization Modeling [60.55856973678002]
  Large Reasoning Models (LRMs) have demonstrated strong capabilities in complex multi-step reasoning.<n>Existing domain adaptation methods, originally designed for earlier instruction-tuned models, often fail to exploit the advanced reasoning patterns of modern LRMs.<n>We propose textbfCALM, a framework that progressively refines LRMs within their native reasoning modes for optimization modeling tasks.
  arXiv  Detail & Related papers  (2025-10-05T13:38:31Z)
• Data-Driven Differential Evolution in Tire Industry Extrusion: Leveraging Surrogate Models [0.0]
  This study proposes a surrogate-based, data-driven methodology for optimizing complex real-world manufacturing systems.<n>Machine learning models are employed to approximate system behavior and construct surrogate models, which are integrated into a tailored metaheuristic approach.<n>Results show that the surrogate-based optimization approach outperforms historical best configurations.
  arXiv  Detail & Related papers  (2025-07-15T10:52:45Z)
• Leveraging Importance Sampling to Detach Alignment Modules from Large Language Models [48.15777554876988]
  Traditional alignment methods often require retraining large pretrained models.<n>We propose a novel textitResidual Alignment Model (textitRAM) that formalizes the alignment process as a type of importance sampling.<n>We develop a resampling algorithm with iterative token-level decoding to address the common first-token latency issue in comparable methods.
  arXiv  Detail & Related papers  (2025-05-26T08:53:02Z)
• LENSLLM: Unveiling Fine-Tuning Dynamics for LLM Selection [11.353302879735862]
  Open-sourced Large Language Models (LLMs) and diverse downstream tasks require efficient model selection.<n>We propose a novel theoretical framework that provides a proper lens to assess the generalization capabilities of LLMs.<n>In particular, we first derive a PAC-Bayesian Generalization Bound that unveils fine-tuning dynamics of LLMs.<n>We then introduce LENSLLM, a Neural Tangent Kernel (NTK)-based Rectified Scaling Model that enables accurate performance predictions.
  arXiv  Detail & Related papers  (2025-05-01T15:07:32Z)
• Model Utility Law: Evaluating LLMs beyond Performance through Mechanism Interpretable Metric [99.56567010306807]
  Large Language Models (LLMs) have become indispensable across academia, industry, and daily applications.<n>One core challenge of evaluation in the large language model (LLM) era is the generalization issue.<n>We propose Model Utilization Index (MUI), a mechanism interpretability enhanced metric that complements traditional performance scores.
  arXiv  Detail & Related papers  (2025-04-10T04:09:47Z)
• Automatic MILP Model Construction for Multi-Robot Task Allocation and Scheduling Based on Large Language Models [13.960259962694126]
  Existing methods face challenges in adapting to dynamic production constraints.<n> enterprises have high privacy requirements for production scheduling data.<n>This study proposes a knowledge-augmented mixed integer lineartemporal (MILP) automated framework.
  arXiv  Detail & Related papers  (2025-03-18T01:45:19Z)
• Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without Premium GPUs [96.68469559192846]
  We present two differently sized MoE large language models (LLMs)<n>Ling-Lite contains 16.8 billion parameters with 2.75 billion activated parameters, while Ling-Plus boasts 290 billion parameters with 28.8 billion activated parameters.<n>We propose innovative methods for (1) optimization of model architecture and training processes, (2) refinement of training anomaly handling, and (3) enhancement of model evaluation efficiency.
  arXiv  Detail & Related papers  (2025-03-07T04:43:39Z)
• Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5% Parameters and 90% Performance [78.48606021719206]
  Mini-InternVL is a series of MLLMs with parameters ranging from 1B to 4B, which achieves 90% of the performance with only 5% of the parameters. We develop a unified adaptation framework for Mini-InternVL, which enables our models to transfer and outperform specialized models in downstream tasks.
  arXiv  Detail & Related papers  (2024-10-21T17:58:20Z)
• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and. Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting. LLMs to downstream tasks. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• Structuring a Training Strategy to Robustify Perception Models with Realistic Image Augmentations [1.5723316845301678]
  This report introduces a novel methodology for training with augmentations to enhance model robustness and performance in such conditions. We present a comprehensive framework that includes identifying weak spots in Machine Learning models, selecting suitable augmentations, and devising effective training strategies. Experimental results demonstrate improvements in model performance, as measured by commonly used metrics such as mean Average Precision (mAP) and mean Intersection over Union (mIoU) on open-source object detection and semantic segmentation models and datasets.
  arXiv  Detail & Related papers  (2024-08-30T14:15:48Z)
• ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling [15.67321902882617]
  We propose a viable path for training open-source LLMs capable of optimization modeling and developing solver codes.<n>This work also introduces IndustryOR, the first industrial benchmark for evaluating LLMs in solving practical OR problems.
  arXiv  Detail & Related papers  (2024-05-28T01:55:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.