基于性能预测的遗传强化学习动态调度方法
Genetic Reinforcement Learning Approach to Dynamic Scheduling Based on Performance Prediction
查看参考文献11篇
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文摘
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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 |
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其他语种文摘
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In 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, genetic operators,RL and controlling parameters carried out the search strategy for the balance of "exploration" and "exploitation". Finally, the simulation results verify the efficiency of GRL scheduling approach |
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来源
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系统仿真学报
,2010,22(12):2809-2812,2820 【核心库】
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关键词
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强化学习
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遗传算法
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预测
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生产周期
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作业车间动态调度
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地址
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1.
沈阳理工大学信息科学与工程学院, 沈阳, 110159
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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, 沈阳, 110016
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1004-731X |
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学科
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自动化技术、计算机技术 |
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基金
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辽宁省自然科学基金
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文献收藏号
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CSCD:4068941
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参考文献 共
11
共1页
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被引
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