Analytics and machine learning in vehicle routing research

@article{Bai2021AnalyticsAM,
  title={Analytics and machine learning in vehicle routing research},
  author={Ruibin Bai and Xinan Chen and Zhi-Long Chen and Tianxiang Cui and Shuhui Gong and Wentao He and Xiaoping Jiang and Huan Jin and Jiahuan Jin and Graham Kendall and Jiawei Li and Zhengyong Lu and Jianfeng Ren and P. Weng and Ning Xue and Huayan Zhang},
  journal={International Journal of Production Research},
  year={2021},
  volume={61},
  pages={4 - 30}
}
The Vehicle Routing Problem (VRP) is one of the most intensively studied combinatorial optimisation problems for which numerous models and algorithms have been proposed. To tackle the complexities, uncertainties and dynamics involved in real-world VRP applications, Machine Learning (ML) methods have been used in combination with analytical approaches to enhance problem formulations and algorithmic performance across different problem solving scenarios. However, the relevant papers are scattered… 

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References

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Simulation results suggest that the proposed Reinforcement Learning Solver for Vehicle Routing Problem (RL SolVeR Pro) is able to obtain better or same level of results, compared to the two best-known heuristics: Clarke-Wright Savings and Sweep Heuristic.

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