摘要
为了强化合同网机制对于分布式调度的学习能力,提出了一种将基本合同网与Q-学习集成的适应性协商机制。运用统一建模语言序列图描述了该机制,并详细阐述了其策略决策过程和学习过程。通过设计嵌入的Q-学习算法的关键要素实现合同网学习机制。通过柔性制造车间内作业在可选单元上动态分配的仿真计算实例表明,相比基本合同网机制,所提机制在平均延误和可选单元间负载平衡这两项系统评价指标上都具有优势。
To intensify the learning ability of Contract Net Protocol（CNP）,an adaptive coordination mechanism named CNP-QL which integrated Q-learning with the basic CNP was presented.The CNP-QL was expressed in Unified Modeling Language（UML） sequence diagram and its decision process as well as learning process were illustrated in detail.Key elements in the embedded Q-learning algorithm were designed to realize the CNP-QL.Computational experiments on tasks dynamic assignment among alternative cells in flexible manufacturing workshop were conducted.Simulation results showed that CNP-QL outperformed the basic CNP in terms of mean tardiness and workload balance among cells.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2011年第6期1257-1263,共7页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(70901058
70832005)
同济大学校基金资助项目(1200219146)~~
