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arxiv: 2605.20849 · v1 · pith:ABBJG6UWnew · submitted 2026-05-20 · 🧮 math.OC

Large Language Models for Operations Research: A Comprehensive Survey

Xianchao Xiu , Jianhao Li , Jun Fan , Wanquan Liu This is my paper

Pith reviewed 2026-05-21 03:53 UTC · model grok-4.3

classification 🧮 math.OC
keywords large language modelsoperations researchmodel formulationalgorithm designsolution verificationbenchmark datasetsoptimization problems
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The pith

Large language models support operations research by aiding in problem formulation, algorithm design, and solution verification.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper provides a systematic review of how large language models can be applied to operations research problems. It details their potential contributions to formulating mathematical models, designing solution algorithms, and verifying outcomes, areas where traditional methods often require significant human expertise. The survey includes discussions of practical uses in various scenarios, available benchmark datasets for evaluation, and outlines key challenges along with suggestions for future work. Readers interested in decision support systems would find value in understanding these emerging capabilities that could make complex optimization more accessible.

Core claim

The central discovery is a comprehensive mapping of large language model applications in operations research, showing how they can handle model formulation for optimization tasks, support algorithm development for solving these models, and perform verification of the computed solutions, while also cataloging real-world applications, datasets, and open research questions.

What carries the argument

The structured roles of large language models in operations research, encompassing model formulation, algorithm design, and solution verification as the primary ways they augment traditional approaches.

If this is right

  • If large language models reliably formulate models, then experts could focus on higher-level decisions rather than routine setup.
  • Algorithm design assistance from these models may enable faster prototyping of solutions for large-scale problems.
  • Solution verification by language models could catch errors that manual checks might miss in complex scenarios.
  • Identified benchmark datasets would facilitate comparative studies and accelerate progress in the field.
  • Future directions outlined could guide research toward more robust integration of these models into decision-making pipelines.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Extending this survey, one might explore how these models perform on stochastic or robust optimization problems not covered in detail.
  • Hybrid approaches combining language models with exact solvers could be tested to improve both creativity and precision in solutions.
  • Implications for education in operations research include using these models as interactive tutors for problem modeling.

Load-bearing premise

The collected studies and described applications of large language models accurately represent their current abilities to manage the expert knowledge demands of traditional operations research problems.

What would settle it

Finding that large language models produce invalid or suboptimal formulations for standard benchmark problems in operations research, such as the traveling salesman problem described in plain language, would indicate the survey overestimates their practical utility.

read the original abstract

Operations Research (OR) serves as a core decision-support methodology for complex systems, with significant applications across mathematics, management science, and computer science. Traditional approaches heavily rely on expert knowledge and often struggle to efficiently solve large-scale and multi-constraint problems. The rapid advancement of Large Language Models (LLMs) in recent years has offered a novel research paradigm to address these challenges. This paper presents a systematic survey of Large Language Models for Operations Research (LLM4OR). We begin by introducing the definition of OR problems and the fundamental principles of LLMs. We then focus on analyzing the roles of LLMs in OR, specifically covering such as model formulation, algorithm design, and solution verification. In addition, we discuss practical applications in representative scenarios and summarize benchmark datasets in this field. Finally, we outline the key challenges and provide perspectives on future research directions. A collection of related literature is available at https://github.com/xianchaoxiu/LLM4OR.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript presents a systematic survey of Large Language Models for Operations Research (LLM4OR). It introduces the definition of OR problems and fundamental principles of LLMs, analyzes LLM roles in model formulation, algorithm design, and solution verification, discusses practical applications in representative scenarios, summarizes benchmark datasets, outlines key challenges, and provides perspectives on future research directions, with an accompanying GitHub repository for related literature.

Significance. If the survey delivers thorough and accurate coverage of this emerging intersection, it would provide a useful consolidation of how LLMs can supplement expert-knowledge-heavy traditional OR methods for large-scale problems. The outlined structure, including benchmarks and challenges, positions the work to guide future research if the literature collection proves comprehensive.

major comments (1)
  1. Abstract: The manuscript describes its contribution as a 'systematic survey' but supplies no information on the literature search methodology, including databases queried, keywords or search strings employed, time period covered, inclusion/exclusion criteria, or any completeness assessment. This omission is load-bearing because the central claim rests on the survey's coverage and accuracy in capturing LLM applications to OR.
minor comments (1)
  1. The GitHub link for the literature collection is a positive step toward reproducibility; consider adding a brief description of its update policy or curation process in the main text.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for this constructive comment. We agree that explicitly documenting the literature search methodology is necessary to substantiate the 'systematic survey' claim and will revise the manuscript to address this.

read point-by-point responses

  1. Referee: Abstract: The manuscript describes its contribution as a 'systematic survey' but supplies no information on the literature search methodology, including databases queried, keywords or search strings employed, time period covered, inclusion/exclusion criteria, or any completeness assessment. This omission is load-bearing because the central claim rests on the survey's coverage and accuracy in capturing LLM applications to OR.

    Authors: We acknowledge the validity of this observation. The current version of the manuscript does not include a dedicated description of the literature collection process. In the revised manuscript we will add a new subsection (e.g., Section 1.3 or an appendix) that details: (1) databases queried (arXiv, Google Scholar, IEEE Xplore, and ACM Digital Library); (2) search strings such as (LLM OR “large language model”) AND (“operations research” OR optimization OR scheduling OR “integer programming”); (3) time period (primarily 2022–2024, reflecting the emergence of capable LLMs); (4) inclusion criteria (peer-reviewed papers, preprints, and workshop papers that apply LLMs to at least one core OR task—formulation, algorithm design, or verification); and (5) exclusion criteria (pure LLM capability papers without OR linkage). We will also note that completeness was cross-checked against the continuously updated GitHub repository. The abstract will be updated with a brief clause referencing this methodology. These additions will be placed early in the paper so readers can immediately assess coverage. revision: yes

Circularity Check

0 steps flagged

No significant circularity in this literature survey

full rationale

This paper is a systematic survey compiling and summarizing external literature on LLM applications to Operations Research. It contains no original derivations, equations, fitted parameters, predictions, or mathematical claims that could reduce to inputs by construction. The structure covers definitions, roles of LLMs, applications, benchmarks, challenges, and future directions, all drawn from cited prior works. No self-citation chains, ansatzes, or uniqueness theorems are invoked in a load-bearing manner; the validity rests on coverage and accurate summarization of independent sources rather than any internal self-referential logic. This is a standard literature review with no circularity patterns present.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The survey rests on standard background definitions of operations research and large language models drawn from prior literature, with no free parameters, ad-hoc axioms, or new entities introduced.

axioms (1)
  • domain assumption Operations Research serves as a core decision-support methodology relying on expert knowledge for complex systems.
    Invoked in the opening definition of OR problems and traditional approaches.

pith-pipeline@v0.9.0 · 5699 in / 1062 out tokens · 42549 ms · 2026-05-21T03:53:02.504461+00:00 · methodology

discussion (0)

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