Scheduling of AGVs in Automated Container Terminal Based on the Deep Deterministic Policy Gradient (DDPG) Using the Convolutional Neural Network (CNN)

Abstract

In order to improve the horizontal transportation efficiency of the terminal Automated Guided Vehicles (AGVs), it is necessary to focus on coordinating the time and space synchronization operation of the loading and unloading of equipment, the transportation of equipment during the operation, and the reduction in the completion time of the task. Traditional scheduling methods limited dynamic response capabilities and were not suitable for handling dynamic terminal operating environments. Therefore, this paper discusses how to use delivery task information and AGVs spatiotemporal information to dynamically schedule AGVs, minimizes the delay time of tasks and AGVs travel time, and proposes a deep reinforcement learning algorithm framework. The framework combines the benefits of real-time response and flexibility of the Convolutional Neural Network (CNN) and the Deep Deterministic Policy Gradient (DDPG) algorithm, and can dynamically adjust AGVs scheduling strategies according to the input spatiotemporal state information. In the framework, firstly, the AGVs scheduling process is defined as a Markov decision process, which analyzes the system’s spatiotemporal state information in detail, introduces assignment heuristic rules, and rewards the reshaping mechanism in order to realize the decoupling of the model and the AGVs dynamic scheduling problem. Then, a multi-channel matrix is built to characterize space–time state information, the CNN is used to generalize and approximate the action value functions of different state information, and the DDPG algorithm is used to achieve the best AGV and container matching in the decision stage. The proposed model and algorithm frame are applied to experiments with different cases. The scheduling performance of the adaptive genetic algorithm and rolling horizon approach is compared. The results show that, compared with a single scheduling rule, the proposed algorithm improves the average performance of task completion time, task delay time, AGVs travel time and task delay rate by 15.63%, 56.16%, 16.36% and 30.22%, respectively; compared with AGA and RHPA, it reduces the tasks completion time by approximately 3.10% and 2.40%.

An Improved DQN Algorithm for Automated Guided Vehicle Pathfinding Problem in Port Environment

“…Zhenming Yang et al [20] developed a simulation model of an AGV transport system and proposed a scheduling algorithm based on time estimation. To improve the efficiency of horizontal transportation of AGVs, Chunlei Chen et al [21] proposed a reinforcement learning framework that combines convolutional neural network and deep deterministic policy gradient algorithm. C. Li et al [22] developed a dynamic task chain scheduling model that improves the energy and time utilization of the system, using a genome-based improved genetic algorithm to solve the joint scheduling problem of charging tasks.…”

Artificial Intelligence in Marine Science and Engineering

“…Traditional scheduling methods limited dynamic response capabilities and were not suitable for handling dynamic terminal operating environments. Therefore, the paper [1] discusses how to use delivery task information and AGVs' spatiotemporal information to dynamically schedule AGVs, minimizes the delay time of tasks and AGVs travel time, and proposes a deep reinforcement learning algorithm framework. The framework combines the benefits of real-time response and flexibility of the Convolutional Neural Network (CNN) and the Deep Deterministic Policy Gradient (DDPG) algorithms and can dynamically adjust AGVs scheduling strategies according to the input spatiotemporal state information.…”

A Deep Reinforcement Learning Strategy Combining Expert Experience Guidance for a Fruit-Picking Manipulator

“…Evan proposed a path-planning method for a multiarm manipulator based on the SAC (soft actor-critic) algorithm with hindsight replay (HER), which is suitable for multiarm manipulators with static and periodically mobile obstacles [16]. Chun proposed a deep reinforcement learning algorithm framework that combined the advantages of convolutional neural network (CNN) and deep deterministic policy gradient (DDPG) algorithms to solve how to use delivery task information and automated guided vehicles (AGVs) travel time in the problem of dynamic scheduling of AGV [17]. Xu proposed a good convergence algorithm based on deep reinforcement learning, and designed a reward function including process rewards, such as a speed tracking reward, which solved the problem of sparse rewards [18].…”