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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.03244 |
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| _version_ | 1866910385496391680 |
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| author | Fan, Zhenan Ghaddar, Bissan Wang, Xinglu Xing, Linzi Zhang, Yong Zhou, Zirui |
| author_facet | Fan, Zhenan Ghaddar, Bissan Wang, Xinglu Xing, Linzi Zhang, Yong Zhou, Zirui |
| contents | The rapid advancement of artificial intelligence (AI) techniques has opened up new opportunities to revolutionize various fields, including operations research (OR). This survey paper explores the integration of AI within the OR process (AI4OR) to enhance its effectiveness and efficiency across multiple stages, such as parameter generation, model formulation, and model optimization. By providing a comprehensive overview of the state-of-the-art and examining the potential of AI to transform OR, this paper aims to inspire further research and innovation in the development of AI-enhanced OR methods and tools. The synergy between AI and OR is poised to drive significant advancements and novel solutions in a multitude of domains, ultimately leading to more effective and efficient decision-making. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03244 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process Fan, Zhenan Ghaddar, Bissan Wang, Xinglu Xing, Linzi Zhang, Yong Zhou, Zirui Optimization and Control Artificial Intelligence The rapid advancement of artificial intelligence (AI) techniques has opened up new opportunities to revolutionize various fields, including operations research (OR). This survey paper explores the integration of AI within the OR process (AI4OR) to enhance its effectiveness and efficiency across multiple stages, such as parameter generation, model formulation, and model optimization. By providing a comprehensive overview of the state-of-the-art and examining the potential of AI to transform OR, this paper aims to inspire further research and innovation in the development of AI-enhanced OR methods and tools. The synergy between AI and OR is poised to drive significant advancements and novel solutions in a multitude of domains, ultimately leading to more effective and efficient decision-making. |
| title | Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process |
| topic | Optimization and Control Artificial Intelligence |
| url | https://arxiv.org/abs/2401.03244 |