计算机工程与应用2024，Vol.60Issue(17)：312-320,9.DOI:10.3778/j.issn.1002-8331.2305-0518
基于CNN的深度强化学习算法求解柔性作业车间调度问题
Deep Reinforcement Learning Algorithm Based on CNN to Solve Flexible Job-Shop Scheduling Problem
摘要
Abstract
When using deep reinforcement learning(DRL)algorithm to solve flexible job-shop scheduling problem(FJSP),the representation of state and action is complex and changeable,which leads to the poor quality.In order to get a better solution,the representation of state and action is further studied,and with the makespan as the optimization goal,a DRL algorithm is designed by using convolutional neural network(CNN)and proximal policy optimization(PPO).Aiming at the complexity of the flexible workshop,a dual-channel state representation method is specially designed.The first channel represents the selected machine of each job,and the second represents the processing order of each job on the selected machine.In the action setting,a machine selection algorithm is designed,which can select the best machine according to the current state and combine with the DRL algorithm to form the action selection.Finally,the examples of Brandimarte show that this algorithm is feasible,and the performance of different scale examples is better,and the solu-tion quality is better than that of common algorithms.关键词
深度强化学习(DRL)/柔性作业车间调度(FJSP)/卷积神经网络(CNN)/近端策略优化(PPO)Key words
deep reinforcement learning(DRL)/flexible job-shop scheduling problem(FJSP)/convolutional neural network(CNN)/proximal policy optimization(PPO)分类
信息技术与安全科学引用本文复制引用
李兴洲,李艳武,谢辉..基于CNN的深度强化学习算法求解柔性作业车间调度问题[J].计算机工程与应用,2024,60(17):312-320,9.基金项目
重庆市教育委员会科学技术研究项目(KJQN202001224) （KJQN202001224）
重庆市三峡库区地质环境监测与灾害预警重点实验室开放基金(YB2020C0102). （YB2020C0102）
