Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem

Add code
Nov 25, 2019
John Holler, Risto Vuorio, Zhiwei Qin, Xiaocheng Tang, Yan Jiao, Tiancheng Jin, Satinder Singh, Chenxi Wang, Jieping Ye

Figure 1 for Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem
Figure 2 for Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem
Figure 3 for Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem
Figure 4 for Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem

Share this with someone who'll enjoy it:

Twitter IconFacebook IconLinkedin IconWhatsapp IconMessenger Icon

Order dispatching and driver repositioning (also known as fleet management) in the face of spatially and temporally varying supply and demand are central to a ride-sharing platform marketplace. Hand-crafting heuristic solutions that account for the dynamics in these resource allocation problems is difficult, and may be better handled by an end-to-end machine learning method. Previous works have explored machine learning methods to the problem from a high-level perspective, where the learning method is responsible for either repositioning the drivers or dispatching orders, and as a further simplification, the drivers are considered independent agents maximizing their own reward functions. In this paper we present a deep reinforcement learning approach for tackling the full fleet management and dispatching problems. In addition to treating the drivers as individual agents, we consider the problem from a system-centric perspective, where a central fleet management agent is responsible for decision-making for all drivers.

* ICDM 2019 Short Paper  
View paper onarxiv icon

Share this with someone who'll enjoy it:

Twitter IconFacebook IconLinkedin IconWhatsapp IconMessenger Icon