| 基于Q-学习的动态单机调度 |
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| 引用本文： | 王世进,孙晟,周炳海,奚立峰. 基于Q-学习的动态单机调度[J]. 上海交通大学学报, 2007, 41(8): 1227-1232,1243 |
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| 作者姓名： | 王世进 孙晟 周炳海 奚立峰 |
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| 作者单位： | 上海交通大学,机械与动力工程学院,上海,200240;上海交通大学,机械与动力工程学院,上海,200240;上海交通大学,机械与动力工程学院,上海,200240;上海交通大学,机械与动力工程学院,上海,200240 |
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| 基金项目： | 国家自然科学基金;教育部跨世纪优秀人才培养计划 |
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| 摘 要： | 针对当前基于Q-学习的Agent生产调度优化研究甚少的现状,利用Q-学习对动态单机调度问题在3种不同系统目标下的调度规则动态选择问题进行了研究.在建立Q-学习与动态单机调度问题映射机制的基础上,通过MATLAB实验仿真,对算法性能进行了评价.仿真结果表明,对于不同的系统调度目标,Q-学习能提高Agent的适应能力,达到单一调度规则无法达到的性能,适合基于Agent的动态生产调度环境.
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| 关 键 词： | Q-学习 强化学习 动态单机调度 调度规则选择 |
| 文章编号： | 1006-2467（2007）08-1227-06 |
| 修稿时间： | 2006-10-14 |
Q-Learning Based Dynamic Single Machine Scheduling |
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| WANG Shi-jin,SUN Sheng,ZHOU Bing-hai,XI Li-feng. Q-Learning Based Dynamic Single Machine Scheduling[J]. Journal of Shanghai Jiaotong University, 2007, 41(8): 1227-1232,1243 |
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| Authors: | WANG Shi-jin SUN Sheng ZHOU Bing-hai XI Li-feng |
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| Affiliation: | School of Mechanical Eng., Shanghai Jiaotong Univ., Shanghai 200240, China |
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| Abstract: | Q-learning was applied to a dynamic single-machine scheduling problem. Corresponding to the environment status change and three predefined system performance measurement, the machine agent that is embedded with Q-learning can select an appropriate dispatching rule dynamically. Based on the model between Q-learning and the dynamic single-machine scheduling problem, the performance of Q-learning was evaluated through simulations in MATLABa environment. The simulation results demonstrate that Q-learning can perform well for different system objectives, which is impossible for single dispatching rule. Therefore, Q-learning is promising for application to the agent-based dynamic production scheduling. |
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| Keywords: | Q-learning reinforcement learning dynamic single machine scheduling dispatching rules(selection) |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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