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Mixed Integer Programming (MIP) has been extensively applied in areas requiring mathematical solvers to address complex instances within tight time constraints.
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Eugene L Lawler and David E Wood, · 1966
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CPLEX User’s Manual, · 1987
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“The traveling salesman problem: An overview of exact and approximate algorithms,”
Gilbert Laporte, · 1992
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“The three-dimensional bin packing problem,”
Silvano Martello, David Pisinger, and Daniele Vigo, · 2000
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“Facility location models for distribution system design,”
Andreas Klose and Andreas Drexl, · 2005
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Outline of an algorithm for integer solutions to linear programs and an algorithm for the mixed integer problem
Ralph E Gomory, · 2010
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“Benchmarks for current linear and mixed integer optimization solvers,”
Josef Jablonskỳ et al., · 2015
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“The vehicle routing problem: State of the art classification and review,”
Kris Braekers, Katrien Ramaekers, and Inneke Van Nieuwenhuyse, · 2016
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“Learning to branch in mixed integer programming,”
Elias Khalil, Pierre Le Bodic, Le Song, George Nemhauser, and Bistra Dilkina, · 2016
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“Vehicle scheduling under stochastic trip times: An approximate dynamic programming approach,”
Fang He, Jie Yang, and Meng Li, · 2018
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“Deep multi-view spatial-temporal network for taxi demand prediction,”
Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li, · 2018
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“What’s new in gurobi 9.0,”
Tobias Achterberg, · 2019
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“Exact combinatorial optimization with graph convolutional neural networks,”
Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, and Andrea Lodi, · 2019
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“Language models are few-shot learners,”
Tom B Brown, · 2020
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“A general large neighborhood search framework for solving integer linear programs,”
Jialin Song, Yisong Yue, Bistra Dilkina, et al., · 2020
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“Reinforcement learning for integer programming: Learning to cut,”
“Cardinal optimizer (copt) user guide,”
Dongdong Ge, Qi Huangfu, Zizhuo Wang, Jian Wu, and Yinyu Ye, · 2022
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“Large language models as optimizers,”
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V Le, Denny Zhou, and Xinyun Chen, · 2023
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al., · 2023
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“A survey for solving mixed integer programming via machine learning,”
Jiayi Zhang, Chang Liu, Xijun Li, Hui-Ling Zhen, Mingxuan Yuan, Yawen Li, and Junchi Yan, · 2023
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“Chain-of-experts: When llms meet complex operations research problems,”
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Yunhao Tang, Shipra Agrawal, and Yuri Faenza, · 2020
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“Smart feasibility pump: Reinforcement learning for (mixed) integer programming,”
Meng Qi, Mengxin Wang, and Zuo-Jun Shen, · 2021
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“Chain-of-thought prompting elicits reasoning in large language models,”
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al., · 2022
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“A survey of job shop scheduling problem: The types and models,”
Hegen Xiong, Shuangyuan Shi, Danni Ren, and Jinjin Hu, · 2022
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“Self-consistency improves chain of thought reasoning in language models,”
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou, · 2022
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Ziyang Xiao, Dongxiang Zhang, Yangjun Wu, Lilin Xu, Yuan Jessica Wang, Xiongwei Han, Xiaojin Fu, Tao Zhong, Jia Zeng, Mingli Song, et al., · 2023
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“Optimus: Optimization modeling using mip solvers and large language models,”
Ali AhmadiTeshnizi, Wenzhi Gao, and Madeleine Udell, · 2023
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“Tree of Thoughts: Deliberate problem solving with large language models,” 2023
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan, · 2023
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“Palm: Scaling language modeling with pathways,”
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al., · 2023
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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al., · 2024
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“large language models are good multi-lingual learners : when llms meet cross-lingual prompts,”
Teng Wang, Zhenqi He, Wing-Yin Yu, Xiaojin Fu, and Xiongwei Han, · 2024
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