哈尔滨工程大学学报2024，Vol.45Issue(5)：996-1004,9.DOI:10.11990/jheu.202212001
基于DQN的自动化集装箱码头自动引导车多目标调度优化
Multiobjective scheduling optimization of AGVs in DQN algorithm-based automated container terminals
摘要
Abstract
Taking the maximum utilization rate of AGV and the minimum energy consumption as the objectives,we establish a mathematical model of AGV scheduling optimization,design seven different scheduling strategies as the space of variable scheduling strategies,refine the state characteristics and the reward function of the AGV schedu-ling problem,and propose a scheduling algorithm based on the variable scheduling strategy of Deep Q-Network(DQN).Results show that compared with the GA and Q-learning algorithms,the scheduling scheme derived based on the DQN scheduling optimization method can improve the utilization rate of AGVs by 14.76％and 19.92％.For energy consumption,the average energy consumption of the scheduling scheme based on DQN is reduced by 16.88％and 10.77％,compared with that of the GA and Q-learning algorithms.By comparing with the fixed scheduling strategy,the average utilization is improved by 12.39％,and the average energy consumption is reduced by 7.58％.Moreover,the solution quality of the proposed method is higher,while the effectiveness of the proposed variable strategy is verified by comparing it with the fixed strategy.关键词
自动化集装箱码头/深度强化学习/自动引导车调度/调度策略Key words
automated container terminal/deep reinforcement learning/AGV scheduling/scheduling strategy分类
交通工程引用本文复制引用
初良勇,梁冬..基于DQN的自动化集装箱码头自动引导车多目标调度优化[J].哈尔滨工程大学学报,2024,45(5):996-1004,9.基金项目
福建省自然科学基金项目(2021J01820) （2021J01820）
国家重点研发计划(2017YFC0805309) （2017YFC0805309）
国家社科基金重大项目(23&ZD138) （23&ZD138）
国家社科基金重点项目(22AZD108) （22AZD108）
福建省新型智库重大项目(24MZKA20). （24MZKA20）
