5 citations · 10 across the 3 of their papers we have counts for
3 papers
cs.AI2022★ 5 cited
Introduction to The Dynamic Pickup and Delivery Problem Benchmark -- ICAPS 2021 Competition
Jianye Hao, Jiawen Lu, Xijun Li +4
The Dynamic Pickup and Delivery Problem (DPDP) is an essential problem within the logistics domain. So far, research on this problem has mainly focused on using artificial data whi…
cs.AI2021★ 4 cited
Learning to Optimize Industry-Scale Dynamic Pickup and Delivery Problems
Xijun Li, Weilin Luo, Mingxuan Yuan +5
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 p…
cs.AI2020★ 1 cited
Bilevel Learning Model Towards Industrial Scheduling
Longkang Li, Hui-Ling Zhen, Mingxuan Yuan +5
Automatic industrial scheduling, aiming at optimizing the sequence of jobs over limited resources, is widely needed in manufacturing industries. However, existing scheduling system…