[24] Wouter Kool, Herke Van Hoof, and Max Welling. Attention, learn to solve routing problems! In International Conference on Learning Representations, 2019.
[25] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, 2017.
[26] 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, 2020.
[27] Minsu Kim, Jinkyoo Park, et al. Learning collaborative policies to solve np-hard routing problems. In Advances in Neural Information Processing Systems, 2021.
[28] 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.
[29] Liang Xin, Wen Song, Zhiguang Cao, and Jie Zhang. Multi-decoder attention model with embedding glimpse for solving vehicle routing problems. In AAAI Conference on Artificial Intelligence, pages 12042-12049, 2021.
[30] Bo Peng, Jiahai Wang, and Zizhen Zhang. A deep reinforcement learning algorithm using dynamic attention model for vehicle routing problems. In ISICA 2019, volume CCIS 1205, pages 636-650, 2020.
[31] Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song. Learning combinatorial optimization algorithms over graphs. In Advances in Neural Information Processing Systems, 2017.
[32] Sahil Manchanda, Akash Mittal, Anuj Dhawan, Sourav Medya, Sayan Ranu, and Ambuj Singh. GCOMB: Learning budget-constrained combinatorial algorithms over billion-sized graphs. In Advances in Neural Information Processing Systems, 2020.
[33] Lu Duan, Yang Zhan, Haoyuan Hu, Yu Gong, Jiangwen Wei, Xiaodong Zhang, and Yinghui Xu. Efficiently solving the practical vehicle routing problem: A novel joint learning approach. In ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 3054-3063, 2020.
[34] Jinbiao Chen, Huanhuan Huang, Zizhen Zhang, and Jiahai Wang. Deep reinforcement learning with two-stage training strategy for practical electric vehicle routing problem with time windows. In International Conference on Parallel Problem Solving from Nature, 2022.
[35] Xinyun Chen and Yuandong Tian. Learning to perform local rewriting for combinatorial optimization. In Advances in Neural Information Processing Systems, 2019.
[36] Hao Lu, Xingwen Zhang, and Shuang Yang. A learning-based iterative method for solving vehicle routing problems. In International Conference on Learning Representations, 2020.
[37] André Hottung and Kevin Tierney. Neural large neighborhood search for the capacitated vehicle routing problem. In European Conference on Artificial Intelligence, 2020.
[38] Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, and Andrew Lim. Learning improvement heuristics for solving routing problems. IEEE Transactions on Neural Networks and Learning Systems, 33(9):5057-5069, 2022.
[39] 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.
[40] Xiaoliang Ma, Qingfu Zhang, Guangdong Tian, Junshan Yang, and Zexuan Zhu. On tchebycheff decomposition approaches for multiobjective evolutionary optimization. IEEE Transactions on Evolutionary Computation, 22(2):226-244, 2017.
[41] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning, pages 1126–1135, 2017.
[42] Ronald J Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3):229-256, 1992.
[43] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In International Conference on Learning Representations, 2015.