ACDRL: An actor–critic deep reinforcement learning approach for solving the energy-aimed train timetable rescheduling problem under random disturbances

ACDRL: An actor–critic deep reinforcement learning approach for solving the energy-aimed train timetable rescheduling problem under random disturbances

In recent years, large-scale and high-density operations have caused a dramatic increase in the energy consumption of metro systems. For overcrowded metro systems, the original energy-optimized timetable is no longer optimal after unexpected dwell disturbances occur. In this paper, we propose an act...

Full description

Bibliographic Details
Main Authors: Jinlin Liao, Guilian Wu, Hao Chen, Shiyuan Ni, Tingting Lin, Lu Tang
Format: Article
Language:English
Published: Elsevier 2022-11-01
Series:Energy Reports
Subjects:
Actor–critic
Deep reinforcement learning
Energy saving
Metro
Random disturbances
Timetable rescheduling
Online Access:http://www.sciencedirect.com/science/article/pii/S2352484722017036

Internet

http://www.sciencedirect.com/science/article/pii/S2352484722017036

Similar Items