NL2OR: Solve Complex Operations Research Problems Using Natural Language Inputs
<|reference_start|>NL2OR: Solve Complex Operations Research Problems Using Natural Language Inputs: 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.<|reference_end|>
@article{li2024nl2or:,
    title={NL2OR: Solve Complex Operations Research Problems Using Natural Language
  Inputs},
    author={Junxuan Li, Ryan Wickman, Sahil Bhatnagar, Raj Kumar Maity, Arko
  Mukherjee},
    journal={arXiv preprint arXiv:2408.07272},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07272},
    primaryClass={cs.AI cs.HC}
}