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Neural solvers based on attention mechanism have demonstrated remarkable effectiveness in solving vehicle routing problems.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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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 · 2016
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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 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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Attention, learn to solve routing problems!
Wouter Kool, Herke Van Hoof, and Max Welling · 2018
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Efficiently solving very large-scale routing problems
Florian Arnold, Michel Gendreau, and Kenneth Sörensen · 2019
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Learning to perform local rewriting for combinatorial optimization
Xinyun Chen and Yuandong Tian · 2019
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Exploratory combinatorial optimization with reinforcement learning
Thomas Barrett, William Clements, Jakob Foerster, and Alex Lvovsky · 2020
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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 · 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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A fast and scalable heuristic for the solution of large-scale capacitated vehicle routing problems
Luca Accorsi and Daniele Vigo · 2021
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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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The transformer network for the traveling salesman problem
Xavier Bresson and Thomas Laurent · 2021
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Generalize a small pre-trained model to arbitrarily large tsp instances
Zhang-Hua Fu, Kai-Bin Qiu, and Hongyuan Zha · 2021
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Efficient active search for combinatorial optimization problems
André Hottung, Yeong-Dae Kwon, and Kevin Tierney · 2021
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Learning collaborative policies to solve np-hard routing problems
Minsu Kim, Jinkyoo Park, et al · 2021
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Learning to delegate for large-scale vehicle routing
Sirui Li, Zhongxia Yan, and Cathy Wu · 2021
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Rl4co: an extensive reinforcement learning for combinatorial optimization benchmark
Federico Berto, Chuanbo Hua, Junyoung Park, Minsu Kim, Hyeonah Kim, Jiwoo Son, Haeyeon Kim, Joungho Kim, and Jinkyoo Park · 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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Chengrui Gao, Haopu Shang, Ke Xue, Dong Li, and Chao Qian · 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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Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, and Jing Tang · 2021
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Learning improvement heuristics for solving routing problems
Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, and Andrew Lim · 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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Simulation-guided beam search for neural combinatorial optimization
Jinho Choo, Yeong-Dae Kwon, Jihoon Kim, Jeongwoo Jae, André Hottung, Kevin Tierney, and Youngjune Gwon · 2022
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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 · 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 problems over time: a survey
Andrea Mor and Maria Grazia Speranza · 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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A prediction-and-scheduling framework for efficient order transfer in logistics
Wenjun Lyu, Haotian Wang, Yiwei Song, Yunhuai Liu, Tian He, and Desheng Zhang · 2023
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H-tsp: Hierarchically solving the large-scale travelling salesman problem
Xuanhao Pan, Yan Jin, Yuandong Ding, Mingxiao Feng, Li Zhao, Lei Song, and Jiang Bian · 2023
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Haoran Ye, Jiarui Wang, Helan Liang, Zhiguang Cao, Yong Li, and Fanzhang Li · 2023
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Neural tsp solver with progressive distillation
Dongxiang Zhang, Ziyang Xiao, Yuan Wang, Mingli Song, and Gang Chen · 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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