[21] Chaitanya K. Joshi and Rishabh Anand. Recent advances in deep learning for routing problems. In ICLR Blog Track, 2022. URL https://iclr-blog-track.github.io/2022/03/25/deep-learning-for-routing-problems/. https://iclr-blog-track.github.io/2022/03/25/deep-learning-for-routing-problems/.
[22] Chaitanya K Joshi, Thomas Laurent, and Xavier Bresson. An efficient graph convolutional network technique for the travelling salesman problem. arXiv preprint arXiv:1906.01227, 2019.
[23] Chaitanya K Joshi, Quentin Cappart, Louis-Martin Rousseau, and Thomas Laurent. Learning the travelling salesperson problem requires rethinking generalization. Constraints, 27(1-2):70-98, 2022.
[24] Minsu Kim, Jinkyoo Park, et al. Learning collaborative policies to solve np-hard routing problems. Advances in Neural Information Processing Systems, 34:10418-10430, 2021.
[25] Minsu Kim, Junyoung Park, and Jinkyoo Park. Sym-nco: Leveraging symmetry for neural combinatorial optimization. arXiv preprint arXiv:2205.13209, 2022.
[26] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
[27] Wouter Kool, Herke van Hoof, and Max Welling. Attention, learn to solve routing problems! In International Conference on Learning Representations, 2019. URL https://openreview.net/forum?id=ByxBFsRqYm.
[28] Wouter Kool, Herke van Hoof, Joaquim Gromicho, and Max Welling. Deep policy dynamic programming for vehicle routing problems. In *Integration of Constraint Programming*, Artificial Intelligence, and Operations Research: 19th International Conference, CPAIOR 2022, Los Angeles, CA, USA, June 20-23, 2022, Proceedings, pages 190-213. Springer, 2022.
[29] Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim, Iljoo Yoon, Youngjune Gwon, and Seungjai Min. Pomo: Policy optimization with multiple optima for reinforcement learning. Advances in Neural Information Processing Systems, 33:21188-21198, 2020.
[30] Yeong-Dae Kwon, Jinho Choo, Iljoo Yoon, Minah Park, Duwon Park, and Youngjune Gwon. Matrix encoding networks for neural combinatorial optimization. In Advances in Neural Information Processing Systems, 2021.
[31] Bingjie Li, Guohua Wu, Yongming He, Mingfeng Fan, and Witold Pedrycz. An overview and experimental study of learning-based optimization algorithms for the vehicle routing problem. IEEE/CAA Journal of Automatica Sinica, 9(7):1115-1138, 2022.
[32] Sirui Li, Zhongxia Yan, and Cathy Wu. Learning to delegate for large-scale vehicle routing. Advances in Neural Information Processing Systems, 34:26198-26211, 2021.
[33] Zhuwen Li, Qifeng Chen, and Vladlen Koltun. Combinatorial optimization with graph convolutional networks and guided tree search. In Advances in Neural Information Processing Systems, page 539, 2018.
[34] Ruiwu Liu, Xiaocen Li, and Kit S Lam. Combinatorial chemistry in drug discovery. Current opinion in chemical biology, 38:117-126, 2017.
[35] Hao Lu, Xingwen Zhang, and Shuang Yang. A learning-based iterative method for solving vehicle routing problems. In International Conference on Learning Representations, 2019.
[36] Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, and Jing Tang. Learning to iteratively solve routing problems with dual-aspect collaborative transformer. In Advances in Neural Information Processing Systems, 2021.
[37] Mohammadreza Nazari, Afshin Oroojlooy, Lawrence Snyder, and Martin Takác. Reinforcement learning for solving the vehicle routing problem. Advances in neural information processing systems, 31, 2018.
[38] Xuanhao Pan, Yan Jin, Yuandong Ding, Mingxiao Feng, Li Zhao, Lei Song, and Jiang Bian. H-tsp: Hierarchically solving the large-scale traveling salesman problem. In AAAI 2023, February 2023. URL https://www.microsoft.com/en-us/research/publication/h-tsp-hierarchically-solving-the-large-scale-traveling-salesman-problem/.
[39] Junyoung Park, Sanjar Bakhtiyar, and Jinkyoo Park. Schedulenet: Learn to solve multi-agent scheduling problems with reinforcement learning. arXiv preprint arXiv:2106.03051, 2021.
[40] Junyoung Park, Jaehyeong Chun, Sang Hun Kim, Youngkook Kim, and Jinkyoo Park. Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning. International Journal of Production Research, 59(11):3360-3377, 2021.
[41] Laurent Perron and Vincent Furnon. Or-tools, 2023. URL https://developers.google.com/optimization/.
[42] Ruizhong Qiu, Zhiqing Sun, and Yiming Yang. Dimes: A differentiable meta solver for combinatorial optimization problems. arXiv preprint arXiv:2210.04123, 2022.