这是indexloc提供的服务，不要输入任何密码
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Artificial Intelligence

arXiv:2408.07272 (cs)
[Submitted on 14 Aug 2024 (v1), last revised 28 Jan 2025 (this version, v2)]

Title:Abstract Operations Research Modeling Using Natural Language Inputs

Authors:Junxuan Li, Ryan Wickman, Sahil Bhatnagar, Raj Kumar Maity, Arko Mukherjee
View PDF HTML (experimental)
Abstract:Operations research (OR) uses mathematical models to enhance decision-making, but developing these models requires expert knowledge and can be time-consuming. Automated mathematical programming (AMP) has emerged to simplify this process, but existing systems have limitations. This paper introduces a novel methodology that uses recent advances in Large Language Model (LLM) to create and edit OR solutions from non-expert user queries expressed using Natural Language. This reduces the need for domain expertise and the time to formulate a problem. The paper presents an end-to-end pipeline, named NL2OR, that generates solutions to OR problems from natural language input, and shares experimental results on several important OR problems.
Subjects: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2408.07272 [cs.AI]
  (or arXiv:2408.07272v2 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2408.07272

Submission history

From: Junxuan Li [view email]
[v1] Wed, 14 Aug 2024 03:42:53 UTC (131 KB)
[v2] Tue, 28 Jan 2025 18:40:26 UTC (163 KB)
Full-text links:

Access Paper:

license icon view license
Current browse context:
cs.AI
< prev   |   next >
new | recent | 2024-08
Change to browse by:
cs
cs.HC

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)