Skip to content

Latest commit

 

History

History
64 lines (42 loc) · 2.71 KB

README.md

File metadata and controls

64 lines (42 loc) · 2.71 KB

Decision Information Meets Large Language Models: The Future of Explainable Operations Research

Operations Research (OR) is vital for decision-making in many industries. While recent OR methods have seen significant improvements in automation and efficiency through integrating Large Language Models (LLMs), they still struggle to produce meaningful explanations. This lack of clarity raises concerns about transparency and trustworthiness in OR applications. To address these challenges, we propose a comprehensive framework, Explainable Operations Research (EOR), emphasizing actionable and understandable explanations accompanying optimization. The core of EOR is the concept of \textit{Decision Information}, which emerges from what-if analysis and focuses on evaluating the impact of complex constraints (or parameters) changes on decision-making. Specifically, we utilize bipartite graphs to quantify the changes in the OR model and adopt LLMs to improve the explanation capabilities. Additionally, we introduce the first industrial benchmark to rigorously evaluate the effectiveness of explanations and analyses in OR, establishing a new standard for transparency and clarity in the field.

Paper in openreview

Run the EOR Code

Setup the Packages and OpenAI API

  1. Install and setup Python Packages.
  2. Install and setup "Gurobi Optimizer" from Gurobi's official website.
  3. Setup your own api_key in the OAI_CONFIG_LIST file.

Run the Code

The examples for the problems on the 1-shot setting are in the examples folder.

  • Option 1: To run the 0/1 shot one by one, run:

    python run-0shot.py or python run-1shot.py

  • Option 2: To run the 0/1 shot for all problems, run:

    python run_all_problems-0shot.py or python run_all_problems-1shot.py

Benchmark Dataset

We provide the 30 problems and 10 queries for each problem in the benchmark folder and the true labels in the True-labels folder.

Baselines

We provide the baseline code (OptiGuide and Standard) in the baselines folder.

Citation

Please cite our paper if you use this code in your work:

@article{zhang2025,
  title={Decision Information Meets Large Language Models: The Future of Explainable Operations Research},
  author={Zhang, Yansen and Kang, Qingcan, and Yu, Wing Yin and Gong, Hailei, and Fu, Xiaojin and Han, Xiongwei and Zhong, Tao and Ma, Chen},
  journal={ICLR},
  year={2025}
}

Acknowledgements

Thanks for this repos when developing this one:

OptiGuide