[1] Paolo Toth and Daniele Vigo. Vehicle routing: problems, methods, and applications. SIAM press, 2014.
[2] Cong Zhang, Yaoxin Wu, Yining Ma, Wen Song, Zhang Le, Zhiguang Cao, and Jie Zhang. A review on learning to solve combinatorial optimisation problems in manufacturing. IET Collaborative Intelligent Manufacturing, 5(1):e12072, 2023.
[3] Wouter Kool, Herke van Hoof, and Max Welling. Attention, learn to solve routing problems! In International Conference on Learning Representations, 2018.
[4] Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim, Iljoo Yoon, Youngjune Gwon, and Seungjai Min. POMO: Policy optimization with multiple optima for reinforcement learning. In Advances in Neural Information Processing Systems, volume 33, pages 21188-21198, 2020.
[5] André Hottung, Yeong-Dae Kwon, and Kevin Tierney. Efficient active search for combinatorial optimization problems. In International Conference on Learning Representations, 2022.
[6] Zhang-Hua Fu, Kai-Bin Qiu, and Hongyuan Zha. Generalize a small pre-trained model to arbitrarily large TSP instances. In AAAI Conference on Artificial Intelligence, 2021.
[7] Ruizhong Qiu, Zhiqing Sun, and Yiming Yang. DIMES: A differentiable meta solver for combinatorial optimization problems. In Advances in Neural Information Processing Systems, volume 35, pages 25531-25546, 2022.
[8] André Hottung and Kevin Tierney. Neural large neighborhood search for routing problems. Artificial Intelligence, page 103786, 2022.
[9] 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, volume 34, pages 11096-11107, 2021.
[10] Qingfu Zhang Xi Lin, Zhiyuan Yang. Pareto set learning for neural multi-objective combinatorial optimization. In International Conference on Learning Representations, 2022.
[11] Jieyi Bi, Yining Ma, Jiahai Wang, Zhiguang Cao, Jinbiao Chen, Yuan Sun, and Yeow Meng Chee. Learning generalizable models for vehicle routing problems via knowledge distillation. In Advances in Neural Information Processing Systems, pages 31226-31238, 2022.
[12] Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Hongliang Guo, Yuejiao Gong, and Yeow Meng Chee. Efficient neural neighborhood search for pickup and delivery problems. In International Joint Conference on Artificial Intelligence, pages 4776-4784, 2022.
[13] Minsu Kim, Junyoung Park, and Jinkyoo Park. Sym-nco: Leveraging symmetry for neural combinatorial optimization. In Advances in Neural Information Processing Systems, volume 35, pages 1936-1949, 2022.
[14] Chaitanya K Joshi, Thomas Laurent, and Xavier Bresson. An efficient graph convolutional network technique for the travelling salesman problem. arxiv preprint arxiv:1906.01227, ArXiV, 2019.
[15] Zhiqing Sun and Yiming Yang. Difusco: Graph-based diffusion solvers for combinatorial optimization. arxiv preprint arxiv:2302.08224, ArXiV, 2023.
[16] Paulo da Costa, Jason Rhuggenaath, Yingqian Zhang, and Alp Eren Akçay. Learning 2-opt heuristics for the traveling salesman problem via deep reinforcement learning. In *Asian Conference on Machine Learning*, pages 465–480, 2020.
[17] Jingyan Sui, Shizhe Ding, Ruizhi Liu, Liming Xu, and Dongbo Bu. Learning 3-opt heuristics for traveling salesman problem via deep reinforcement learning. In Asian Conference on Machine Learning, volume 157, pages 1301-1316, 2021.
[18] Zbigniew Michalewicz et al. Do not kill unfeasible individuals. In Proceedings of the Fourth Intelligent Information Systems Workshop, pages 110–123, 1995.