Nam Le

Vehicle Routing Problem (VRP)

Nam Le
Table of Contents

Vehicle Routing Problem (VRP) #

The Vehicle Routing Problem is about finding optimal routes for a fleet of vehicles to serve a set of customers, a fundamental problem in logistics and transportation.

Recent Literature #

  1. Learning to Perform Local Rewriting for Combinatorial Optimization. NeurIPS, 2019. paper, code

    Chen, Xinyun and Tian, Yuandong.

  2. Deep Reinforcement Learning for the Electric Vehicle Routing Problem with Time Windows. Arxiv, 2020. paper

    Lin, Bo and Ghaddar, Bissan and Nathwani, Jatin.

  3. Efficiently Solving the Practical,Vehicle Routing Problem: A Novel Joint Learning Approach. KDD, 2020. paper

    Lu Duan, Yang Zhan, Haoyuan Hu, Yu Gong, Jiangwen Wei, Xiaodong Zhang, Yinghui Xu

  4. Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing NeurIPS, 2020. paper, code

    Arthur Delarue, Ross Anderson, Christian Tjandraatmadja

  5. A Learning-based Iterative Method for Solving Vehicle Routing Problems ICLR, 2020. paper

    Lu, Hao and Zhang, Xingwen and Yang, Shuang

  6. Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem Arxiv, 2020. paper

    Hottung, Andre and Tierney, Kevin

  7. Learning Improvement Heuristics for Solving Routing Problems TNNLS, 2021. journal

    Wu, Yaoxin and Song, Wen and Cao, Zhiguang and Zhang, Jie and Lim, Andrew

  8. Reinforcement Learning for Route Optimization with Robustness Guarantees IJCAI, 2021. paper

    Jacobs, Tobias and Alesiani, Francesco and Ermis, Gulcin

  9. Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems. AAAI, 2021. paper, code

    Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang

  10. Analytics and Machine Learning in Vehicle Routing Research Arxiv, 2021. paper

    Bai, Ruibin and Chen, Xinan and Chen, Zhi-Long and Cui, Tianxiang and Gong, Shuhui and He, Wentao and Jiang, Xiaoping and Jin, Huan and Jin, Jiahuan and Kendall, Graham and others

  11. RP-DQN: An application of Q-Learning to Vehicle Routing Problems Arxiv, 2021. paper

    Bdeir, Ahmad and Boeder, Simon and Dernedde, Tim and Tkachuk, Kirill and Falkner, Jonas K and Schmidt-Thieme, Lars

  12. Deep Policy Dynamic Programming for Vehicle Routing Problems Arxiv, 2021. paper

    Kool, Wouter and van Hoof, Herke and Gromicho, Joaquim and Welling, Max

  13. Learning to Delegate for Large-scale Vehicle Routing NeurIPS, 2021. paper

    Li, Sirui and Yan, Zhongxia and Wu, Cathy

  14. Learning a Latent Search Space for Routing Problems using Variational Autoencoders ICLR, 2021. paper

    Hottung, Andre and Bhandari, Bhanu and Tierney, Kevin

  15. Preference Conditioned Neural Multi-objective Combinatorial Optimization ICLR, 2022. paper

    Lin, Xi and Yang, Zhiyuan and Zhang, Qingfu

  16. Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation NeurIPS, 2022. paper, code

    Bi, Jieyi and Ma, Yining and Wang, Jiahai and Cao, Zhiguang and Chen, Jinbiao and Sun, Yuan and Chee, Yeow Meng

  17. Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization NeurIPS, 2022. paper, code

    Kim, Minsu and Park, Junyoung and Park, Jinkyoo

  18. Simulation-guided Beam Search for Neural Combinatorial Optimization NeurIPS, 2022. paper, code

    Choo, Jinho and Kwon, Yeong-Dae and Kim, Jihoon and Jae, Jeongwoo and Hottung, Andr{'e} and Tierney, Kevin and Gwon, Youngjune

  19. Learning to CROSS exchange to solve min-max vehicle routing problems ICLR, 2023. paper

    Kim, Minjun and Park, Junyoung and Park, Jinkyoo

  20. Generalize Learned Heuristics to Solve Large-scale Vehicle Routing Problems in Real-time ICLR, 2023. paper

    Hou, Qingchun and Yang, Jingwei and Su, Yiqiang and Wang, Xiaoqing and Deng, Yuming

  21. Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization ICML, 2023. paper

    Son, Jiwoo and Kim, Minsu and Kim, Hyeonah and Park, Jinkyoo

  22. Towards Omni-generalizable Neural Methods for Vehicle Routing Problems ICML, 2023. paper, code

    Zhou Jianan, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang

  23. DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization NeurIPS, 2023. paper, code

    Ye, Haoran and Wang, Jiarui and Cao, Zhiguang and Liang, Helan and Li, Yong

  24. Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization NeurIPS, 2023. paper

    Grinsztajn, Nathan and Furelos-Blanco, Daniel and Surana, Shikha and Bonnet, Cl{'e}ment and Barrett, Thomas D

  25. Combinatorial Optimization with Policy Adaptation using Latent Space Search NeurIPS, 2023. paper

    Chalumeau, Felix and Surana, Shikha and Bonnet, Cl{'e}ment and Grinsztajn, Nathan and Pretorius, Arnu and Laterre, Alexandre and Barrett, Thomas D

  26. Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization NeurIPS, 2023. paper, code

    Chen, Jinbiao and Wang, Jiahai and Zhang, Zizhen and Cao, Zhiguang and Ye, Te and Chen, Siyuan

  27. BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization NeurIPS, 2023. paper, code

    Drakulic, Darko and Michel, Sofia and Mai, Florian and Sors, Arnaud and Andreoli, Jean-Marc

  28. Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization NeurIPS, 2023. paper, code

    Luo, Fu and Lin, Xi and Liu, Fei and Zhang, Qingfu and Wang, Zhenkun

  29. Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement NeurIPS, 2023. paper, code

    Chen, Jinbiao and Zhang, Zizhen and Cao, Zhiguang and Wu, Yaoxin and Ma, Yining and Ye, Te and Wang, Jiahai

  30. Ensemble-based Deep Reinforcement Learning for Vehicle Routing Problems under Distribution Shift NeurIPS, 2023. paper

    Jiang, Yuan and Cao, Zhiguang and Wu, Yaoxin and Song, Wen and Zhang, Jie

  31. Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt NeurIPS, 2023. paper, code

    Ma, Yining and Cao, Zhiguang and Chee, Yeow Meng

  32. Learning to Prune Electric Vehicle Routing Problems LION, 2023. paper

    James Fitzpatrick, Deepak Ajwani, Paula Carroll

  33. GLOP: Learning Global Partition and Local Construction for Solving Large-Scale Routing Problems in Real-Time AAAI, 2024. paper, code

    Haoran Ye, Jiarui Wang, Helan Liang, Zhiguang Cao, Yong Li, Fanzhang Li

  34. Distilling Autoregressive Models to Obtain High-Performance Non-autoregressive Solvers for Vehicle Routing Problems with Faster Inference Speed AAAI, 2024. paper, code

    Yubin Xiao, Di Wang, Boyang Li, Mingzhao Wang, Xuan Wu, Changliang Zhou, You Zhou

  35. Neural Combinatorial Optimization for Robust Routing Problem with Uncertain Travel Times NeurIPS, 2024. paper

    Pei Xiao, Zizhen Zhang, Jinbiao Chen, Jiahai Wang, Zhenzhen Zhang

  36. Collaboration! Towards Robust Neural Methods for Routing Problems NeurIPS, 2024. paper, code

    Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhiqi Shen

  37. UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization Problems NeurIPS, 2024. paper, code

    Zhi Zheng, Changliang Zhou, Tong Xialiang, Mingxuan Yuan, Zhenkun Wang

  38. A Scalable Learning Approach for the Capacitated Vehicle Routing Problem Computers and Operations Research, 2024. journal

    James Fitzpatrick, Deepak Ajwani, Paula Carroll

  39. A Neural Column Generation Approach to the Vehicle Routing Problem with Two-Dimensional Loading and Last-In-First-Out Constraints IJCAI, 2024. paper, code

    Yifan Xia, Xiangyi Zhang

  40. Rethinking Neural Multi-Objective Combinatorial Optimization via Neat Weight Embedding ICLR, 2025. paper

    Jinbiao Chen, Zhiguang Cao, Jiahai Wang, Yaoxin Wu, Hanzhang Qin, Zizhen Zhang, Yue-Jiao Gong

  41. Boosting Neural Combinatorial Optimization for Large-Scale Vehicle Routing Problems ICLR, 2025. paper

    Fu Luo, Xi Lin, Yaoxin Wu, Zhenkun Wang, Tong Xialiang, Mingxuan Yuan, Qingfu Zhang

  42. ⭐COExpander: Adaptive Solution Expansion for Combinatorial Optimization ICML, 2025. paper, code

    Jiale Ma and Wenzheng Pan and Yang Li and Junchi Yan

  43. ⭐ML4CO-Bench-101: Benchmark Machine Learning for Classic Combinatorial Problems on Graphs NeurIPS, 2025. paper, code

    Jiale Ma and Wenzheng Pan and Yang Li and Junchi Yan

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