Learning to Optimize Industry-Scale Dynamic Pickup and Delivery Problems

@article{Li2021LearningTO,
  title={Learning to Optimize Industry-Scale Dynamic Pickup and Delivery Problems},
  author={Xijun Li and Weilin Luo and Mingxuan Yuan and Jun Wang and Jiawen Lu and Jie Wang and Jinhu Lu and Jia Zeng},
  journal={2021 IEEE 37th International Conference on Data Engineering (ICDE)},
  year={2021},
  pages={2511-2522}
}
  • Xijun Li, Weilin Luo, Jia Zeng
  • Published 1 April 2021
  • Computer Science
  • 2021 IEEE 37th International Conference on Data Engineering (ICDE)
The Dynamic Pickup and Delivery Problem (DPDP) is aimed at dynamically scheduling vehicles among multiple sites in order to minimize the cost when delivery orders are not known a priori. Although DPDP plays an important role in modern logistics and supply chain management, state-of-the-art DPDP algorithms are still limited on their solution quality and efficiency. In practice, they fail to provide a scalable solution as the numbers of vehicles and sites become large. In this paper, we propose a… 

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