Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.09994 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916613900468224 |
|---|---|
| author | Zhang, Yansen Kang, Qingcan Yu, Wing Yin Gong, Hailei Fu, Xiaojin Han, Xiongwei Zhong, Tao Ma, Chen |
| author_facet | Zhang, Yansen Kang, Qingcan Yu, Wing Yin Gong, Hailei Fu, Xiaojin Han, Xiongwei Zhong, Tao Ma, Chen |
| contents | 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 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09994 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Decision Information Meets Large Language Models: The Future of Explainable Operations Research Zhang, Yansen Kang, Qingcan Yu, Wing Yin Gong, Hailei Fu, Xiaojin Han, Xiongwei Zhong, Tao Ma, Chen Artificial Intelligence 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 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. |
| title | Decision Information Meets Large Language Models: The Future of Explainable Operations Research |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2502.09994 |