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Machine learning has been adapted to help solve NP-hard combinatorial optimization problems.
The truck dispatching problem
George B Dantzig and John H Ramser · 1959
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TSPLIB - A traveling salesman problem library
Gerhard Reinelt · 1991
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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An effective implementation of the lin–kernighan traveling salesman heuristic
Keld Helsgaun · 2000
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Concorde TSP solver
David Applegate, Robert Bixby, Vasek Chvatal, and William Cook · 2006
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Pointer networks
Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly · 2015
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Neural combinatorial optimization with reinforcement learning
Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio · 2017
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An extension of the lin-kernighan-helsgaun TSP solver for constrained traveling salesman and vehicle routing problems
Keld Helsgaun · 2017
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Learning combinatorial optimization algorithms over graphs
Elias B. Khalil, Hanjun Dai, Yuyu Zhang, Bistra Dilkina, and Le Song · 2017
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New benchmark instances for the capacitated vehicle routing problem
Eduardo Uchoa, Diego Pecin, Artur Pessoa, Marcus Poggi, Thibaut Vidal, and Anand Subramanian · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Efficiently solving very large-scale routing problems
Florian Arnold, Michel Gendreau, and Kenneth Sörensen · 2019
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Evolving diverse TSP instances by means of novel and creative mutation operators
Jakob Bossek, Pascal Kerschke, Aneta Neumann, Markus Wagner, Frank Neumann, and Heike Trautmann · 2019
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An efficient graph convolutional network technique for the travelling salesman problem
Chaitanya K. Joshi, Thomas Laurent, and Xavier Bresson · 2019
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Attention, learn to solve routing problems!
Wouter Kool, Herke van Hoof, and Max Welling · 2019
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A learning-based iterative method for solving vehicle routing problems
Hao Lu, Xingwen Zhang, and Shuang Yang · 2019
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Slack induction by string removals for vehicle routing problems
Jan Christiaens and Greet Vanden Berghe · 2020
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POMO: Policy optimization with multiple optima for reinforcement learning
Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim, Iljoo Yoon, Youngjune Gwon, and Seungjai Min · 2020
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Learning to dispatch for job shop scheduling via deep reinforcement learning
Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, and Xu Chi · 2020
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Machine learning for combinatorial optimization: a methodological tour d’horizon
Yoshua Bengio, Andrea Lodi, and Antoine Prouvost · 2021
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Generalize a small pre-trained model to arbitrarily large TSP instances
Select and optimize: Learning to aolve large-scale TSP instances
Hanni Cheng, Haosi Zheng, Ya Cong, Weihao Jiang, and Shiliang Pu · 2023
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BQ-NCO: Bisimulation quotienting for generalizable neural combinatorial optimization
Darko Drakulic, Sofia Michel, Florian Mai, Arnaud Sors, and Jean-Marc Andreoli · 2023
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Maml2: meta reinforcement learning via meta-learning for task categories
Qiming Fu, Zhechao Wang, Nengwei Fang, Bin Xing, Xiao Zhang, and Jianping Chen · 2023
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Generalize learned heuristics to solve large-scale vehicle routing problems in real-time
Qingchun Hou, Jingwei Yang, Yiqiang Su, Xiaoqing Wang, and Yuming Deng · 2023
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Ensemble-based deep reinforcement learning for vehicle routing problems under distribution shift
Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Wen Song, and Jie Zhang · 2023
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Zhang-Hua Fu, Kai-Bin Qiu, and Hongyuan Zha · 2021
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Learning generalizable models for vehicle routing problems via knowledge distillation
Jieyi Bi, Yining Ma, Jiahai Wang, Zhiguang Cao, Jinbiao Chen, Yuan Sun, and Yeow Meng Chee · 2022
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Learning to solve routing problems via distributionally robust optimization
Yuan Jiang, Yaoxin Wu, Zhiguang Cao, and Jie Zhang · 2022
Cited alongside, same era.
Learning the travelling salesperson problem requires rethinking generalization
Chaitanya K. Joshi, Quentin Cappart, Louis-Martin Rousseau, and Thomas Laurent · 2022
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Sym-NCO: Leveraging symmetricity for neural combinatorial optimization
Minsu Kim, Junyoung Park, and Jinkyoo Park · 2022
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Vehicle routing problem and related algorithms for logistics distribution: A literature review and classification
Grigorios D Konstantakopoulos, Sotiris P Gayialis, and Evripidis P Kechagias · 2022
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On the generalization of neural combinatorial optimization heuristics
Sahil Manchanda, Sofia Michel, Darko Drakulic, and Jean-Marc Andreoli · 2022
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Yuan Jiang, Zhiguang Cao, Yaoxin Wu, and Jie Zhang · 2023
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Pointerformer: Deep reinforced multi-pointer transformer for the traveling salesman problem
Yan Jin, Yuandong Ding, Xuanhao Pan, Kun He, Li Zhao, Tao Qin, Lei Song, and Jiang Bian · 2023
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From distribution learning in training to gradient search in testing for combinatorial optimization
Yang Li, Jinpei Guo, Runzhong Wang, and Junchi Yan · 2023
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How good is neural combinatorial optimization? A systematic evaluation on the traveling salesman problem
Shengcai Liu, Yu Zhang, Ke Tang, and Xin Yao · 2023
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Neural combinatorial optimization with heavy decoder: Toward large scale generalization
Fu Luo, Xi Lin, Fei Liu, Qingfu Zhang, and Zhenkun Wang · 2023
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Learning to search feasible and infeasible regions of routing problems with flexible neural k-opt
Yining Ma, Zhiguang Cao, and Yeow Meng Chee · 2023
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Meta-sage: Scale meta-learning scheduled adaptation with guided exploration for mitigating scale shift on combinatorial optimization
Jiwoo Son, Minsu Kim, Hyeonah Kim, and Jinkyoo Park · 2023
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Stochastic economic lot scheduling via self-attention based deep reinforcement learning
Wen Song, Nan Mi, Qiqiang Li, Jing Zhuang, and Zhiguang Cao · 2023
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DIFUSCO: Graph-based diffusion solvers for combinatorial optimization
Zhiqing Sun and Yiming Yang · 2023
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Towards omni-generalizable neural methods for vehicle routing problems
Jianan Zhou, Yaoxin Wu, Wen Song, Zhiguang Cao, and Jie Zhang · 2023
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