轻工机械2025，Vol.43Issue(3)：97-103,7.DOI:10.3969/j.issn.1005-2895.2025.03.014
基于改进DQN算法的柔性作业车间调度问题
Flexible Job Shop Scheduling Problem Based on Improved DQN Algorithm
摘要
Abstract
To improve production efficiency and reduce energy consumption of multi-objective flexible job shop scheduling,the research group proposed a multi-objective flexible job shop scheduling method based on deep reinforcement learning.To minimize the maximum completion time and energy consumption,a flexible job shop scheduling model was established,and the objective function was solved by an improved Deep Q-Network(DQN)algorithm.Firstly,the state and action space were defined,and the state and action information of the scheduling problem were embedded into the DQN model for training.For each generation of scheduling schemes,Non-Dominated Sorting Genetic Algorithm-Ⅱ(NSGA-Ⅱ)was used to map the schemes to multi-objective frontiers and update the reward in experience playback to further optimize the Q-values update of DQN.With the training progressed,DQN gradually optimized the scheduling scheme and outputted an optimal scheduling strategy based on minimizing the maximum completion time and the minimum energy consumption.The example shows that the improved DQN algorithm can find the scheduling scheme that meets the multi-objective optimization more quickly and efficiently.关键词
车间调度/柔性作业车间/多目标优化/深度强化学习/NSGA-Ⅱ算法Key words
job shop scheduling/flexible job shop/multi-objective optimization/deep reinforcement learning/NSGA-Ⅱ(Non-Dominated Sorting Genetic Algorithm-Ⅱ)分类
机械工程引用本文复制引用
王强,李仁旺..基于改进DQN算法的柔性作业车间调度问题[J].轻工机械,2025,43(3):97-103,7.基金项目
浙江省2023年度"尖兵""领燕"研发攻关计划(2022C01SA111123) （2022C01SA111123）
国家自然科学基金资助项目(51475434). （51475434）
