Optimization of shunting operation plan in large freight train depot based on DQN algorithm
Jiandong Qiu, Shusheng Xu, Minan Tang, Jiaxuan Liu, Hailong Song
TL;DR
This paper uses deep reinforcement learning to optimize shunting operations in large freight train depots, resulting in more efficient plans compared to traditional methods.
Contribution
A novel DQN-based approach is proposed for optimizing shunting operation plans in freight train depots.
Findings
DQN produces shunting plans with 10% fewer hooks compared to OPC and 5% fewer than BST.
DQN outperforms B&B in solving time and reduces coupling and slipping operations by 5.3% and 2.9%, respectively.
The proposed DQN method provides better quality shunting operation plans for large freight train depots.
Abstract
Shunting operation plan is the main daily work of the freight train depot, the optimization of shunting operation plan is of great significance to improve the efficiency of railway operation and production and transportation. In this paper, the deep reinforcement learning (DRL) environment and model of shunting operation problem are constructed by three elements: action, state and reward, taking shunting locomotive as the agent, the lane number of the fall-down train group as the action, the fall-down conditions of the train group as the state, and design the reward function based on the total number of shunting hooks generated after the group’s descent and reorganization. The model is solved using the Deep Q network (DQN) algorithm with the objective of minimizing the number of shunting hooks, the optimal shunting operation plan can be solved after sufficient training. DQN is verified…
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Taxonomy
TopicsElevator Systems and Control · Railway Systems and Energy Efficiency · Assembly Line Balancing Optimization