首页|Crowd Sourcing Dynamic Pickup & Delivery Problem considering Task Buffering and Drivers' Rejection-Application of Multi-agent Reinforcement Learning-
Crowd Sourcing Dynamic Pickup & Delivery Problem considering Task Buffering and Drivers' Rejection-Application of Multi-agent Reinforcement Learning-
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
In the last decade, dynamic and pickup delivery problem with crowd sourcing has been focused on as a means of securing employment opportunities in the field of last mile delivery. However, only a few studies consider both the driver's refusal right and the buffering strategy. This paper aims at improving the performance involving both of the above. We propose a driver-task matching algorithm that complies with the delivery time constraints using multiagent reinforcement learning. Numerical experiments on the model show that the proposed MARL method could be more effective than the FIFO and the RANK allocation methods.
NSTL
