工业工程2024，Vol.27Issue(1)：78-85,103,9.DOI:10.3969/j.issn.1007-7375.230101
基于深度强化学习的柔性作业车间节能调度研究
Energy-efficient Flexible Job-shop Scheduling Based on Deep Reinforcement Learning
摘要
Abstract
The current research on energy-efficient flexible job-shop scheduling problems(EFJSPs)cannot make full use of historical production data,and is insufficiently adaptable to the complex,dynamic and changeable job-shop production environment.In view of this,deep reinforcement learning is introduced to solve EFJSPs,where a representative method named deep Q-network(DQN)is utilized.First,EFJSP is transformed into a Markov decision process corresponding to reinforcement learning.Moreover,the state values characterizing the job-shop production states are extracted as inputs of a neural network.By fitting the state value function through the neural network,compound scheduling action rules are output to achieve the selection of workpieces and processing machines.Furthermore,scheduling action rules and reward functions are utilized to jointly optimize the total production energy consumption.Finally,solutions of the proposed method are compared with those using typical intelligent optimization algorithms,such as non-dominated sorting genetic algorithm,hyper-heuristic genetic algorithm and multi-objective wolf pack algorithm,in three cases with different scales.Results demonstrate the powerful search capability of DQN algorithm,and the distribution of optimal solutions is consistent with the optimization objective obtained by the proposed EJFSP model.These verify the effectiveness of the utilized DQN method.关键词
柔性作业车间节能调度/深度强化学习/深度Q网络/马尔科夫决策Key words
energy-efficient flexible job-shop scheduling/deep reinforcement learning/deep Q-network/Markov decision分类
管理科学引用本文复制引用
张中伟,李艺,高增恩,武照云..基于深度强化学习的柔性作业车间节能调度研究[J].工业工程,2024,27(1):78-85,103,9.基金项目
国家自然科学基金资助项目(U1704156) （U1704156）
河南省科技攻关计划资助项目(212102210357) （212102210357）
河南省高等学校重点科研资助项目(23A460003) （23A460003）
河南省超硬磨料磨削装备重点实验室开放课题资助项目(JDKFJJ2022012) （JDKFJJ2022012）
