[17] Keld Helsgaun. An Extension of the Lin-Kernighan-Helsgaun TSP Solver for Constrained Traveling Salesman and Vehicle Routing Problems. Technical report, Roskilde University, Roskilde, Denmark, 2017.
[18] Qingchun Hou, Jingwei Yang, Yiqiang Su, Xiaoqing Wang, and Yuming Deng. Generalize Learned Heuristics to Solve Large-scale Vehicle Routing Problems in Real-time. In The Eleventh International Conference on Learning Representations, September 2022.
[19] Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier J. Henaff, Matthew Botvinick, Andrew Zisserman, Oriol Vinyals, and Joao Carreira. Perceiver IO: A General Architecture for Structured Inputs & Outputs. In International Conference on Learning Representations, January 2022.
[20] Yuan Jiang, Zhiguang Cao, and Jie Zhang. Solving 3D Bin Packing Problem via Multimodal Deep Reinforcement Learning. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS '21, pages 1548-1550, Richland, SC, May 2021. International Foundation for Autonomous Agents and Multiagent Systems. ISBN 978-1-4503-8307-3.
[21] Chaitanya K. Joshi, Thomas Laurent, and Xavier Bresson. An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem. arXiv:1906.01227 [cs, stat], June 2019.
[22] 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, April 2022. ISSN 1383-7133.
[23] Elias Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song. Learning Combinatorial Optimization Algorithms over Graphs. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 6348-6358. Curran Associates, Inc., 2017.
[24] Minsu Kim, Junyoung Park, and Jinkyoo Park. Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization. In Advances in Neural Information Processing Systems, New Orleans, December 2022.
[25] Diederik P. Kingma and Jimmy Ba. Adam: A Method for Stochastic Optimization. In Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, May 2015.
[26] Gorka Kobeaga, María Merino, and Jose A. Lozano. An efficient evolutionary algorithm for the orienteering problem. Computers & Operations Research, 90:42-59, February 2018. ISSN 0305-0548.
[27] Andrey Kolobov, Mausam, and Daniel S. Weld. A theory of goal-oriented MDPs with dead ends. In Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, UAI'12, pages 438-447, Arlington, Virginia, USA, August 2012. AUAI Press. ISBN 978-0-9749039-8-9.
[28] Wouter Kool, Herke van Hoof, and Max Welling. Attention, Learn to Solve Routing Problems! In International Conference on Learning Representations, 2019.
[29] 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. Curran Associates, Inc., 2020.
[30] Sirui Li, Zhongxia Yan, and Cathy Wu. Learning to delegate for large-scale vehicle routing. In Advances in Neural Information Processing Systems 34 Pre-Proceedings, 2021.
[31] Minghuan Liu, Menghui Zhu, and Weinan Zhang. Goal-Conditioned Reinforcement Learning: Problems and Solutions. In Thirty-First International Joint Conference on Artificial Intelligence, volume 6, pages 5502–5511, July 2022.