SIA OpenIR  > 智能产线与系统研究室
基于性能预测的遗传强化学习动态调度方法
Alternative TitleGenetic Reinforcement Learning Approach to Dynamic Scheduling Based on Performance Prediction
魏英姿; 谷侃锋
Department现代装备研究室
Source Publication系统仿真学报
ISSN1004-731X
2010
Volume22Issue:12Pages:2809-2812
Indexed ByCSCD
CSCD IDCSCD:4068941
Contribution Rank2
Funding Organization辽宁省自然科学基金项目(20092060)
Keyword强化学习 遗传算法 预测 生产周期 作业车间动态调度
Abstract针对作业车间动态调度问题,在模式驱动调度的框架下,提出遗传强化学习动态调度方法。首先,采用优先规则编码的染色体表达问题的解,将染色体分割成基因模式作为分阶段调度算法的状态模式;其次,设计性能预测变量,构建启发式立即回报函数,引导和加快遗传强化学习算法的搜索进程;再次,设置遗传算子、强化学习及其相关参数以实现搜索过程"开采"与"探索"之间的平衡;最后,仿真实验结果验证了遗传强化学习调度方法的有效性。
Other AbstractIn the framework of pattern driven scheduling,a genetic reinforcement learning (GRL) approach to schedule the job in the dynamical job-shop was proposed.First,the chromosome was coded by preference rules-based representation for the problem.The chromosome was divided into gene schema as state patterns for the multi-phase scheduling system.Secondly,a performance predictive variable to construct instant reward function was designed which was used to guide the learning system to progress rapidly.Thirdly,geneti...
Language中文
Citation statistics
Cited Times:3[CSCD]   [CSCD Record]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/7514
Collection智能产线与系统研究室
Corresponding Author魏英姿
Affiliation1.沈阳理工大学信息科学与工程学院
2.中国科学院沈阳自动化研究所沈阳现代装备研究设计中心
Recommended Citation
GB/T 7714
魏英姿,谷侃锋. 基于性能预测的遗传强化学习动态调度方法[J]. 系统仿真学报,2010,22(12):2809-2812.
APA 魏英姿,&谷侃锋.(2010).基于性能预测的遗传强化学习动态调度方法.系统仿真学报,22(12),2809-2812.
MLA 魏英姿,et al."基于性能预测的遗传强化学习动态调度方法".系统仿真学报 22.12(2010):2809-2812.
Files in This Item: Download All
File Name/Size DocType Version Access License
ZWQK000909.pdf(956KB) 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[魏英姿]'s Articles
[谷侃锋]'s Articles
Baidu academic
Similar articles in Baidu academic
[魏英姿]'s Articles
[谷侃锋]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[魏英姿]'s Articles
[谷侃锋]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: ZWQK000909.pdf
Format: Adobe PDF
This file does not support browsing at this time
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.