Optimization and Performance Evaluation of Network Models

网络模型的优化和性能评估

• 批准号: 9972957
• 负责人: Sean Meyn
• 金额: $ 23.36万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-08-15 至 2002-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9972957&HistoricalAwards=false
• 关键词: Optimization; Performance; Evaluation; Network; Models

ECS-9972957MeynThe proposed research will consider the development of recent approaches to optimal control, reinforceraent learning, and performance evaluation for complex systems. The methods to be employed are based upon recent work conducted by the PI on Markov chain stability theory, linear programming methods, and fluid model approximation.Research on dynamic optimization will be based on a recent discovery of the PI which establishes a relationship between the optimal control of a stochastic model, and the optimal control of a simpler 'leaky bucket' fluid model. The latter gives rise to a constrained L1 optimal control problem which can be solved in many practical examples.The optimization theory will be improved through a deeper analysis of linear programming methods for approximating the value function in the optimal control problem of interest.Reinforcement learning algorithms will be considered for policy improvement in stochastic models. The PI will again base the analysis on fluid model approximations.Specific applications include the development of scheduling and routing algorithms for multiclass queueing networks. The algorithms will be based upon translations of fluid policies; tranlations of fluid value function approximations; and the application of reinforcement learning techniques.

ECS-9972957Meyn拟议的研究将考虑开发最佳控制，增强学习和对复杂系统的绩效评估的方法。 所采用的方法是基于PI对马尔可夫链稳定理论，线性编程方法和流体模型近似的最新工作。对动态优化进行研究将基于对PI的最新发现，该发现建立了对随机模型的最佳控制与简单的“漏水型桶”流体模型的最佳控制之间的关系。 后者产生了有限的L1最佳控制问题，可以在许多实际示例中解决。优化理论将通过对线性编程方法进行更深入的分析来改进，以近似于利益的最佳控制问题中的价值函数。提出学习算法将在随机模型中考虑进行策略改进。 PI将再次将分析基于流体模型近似值。特定的应用程序包括开发多类排队网络的调度和路由算法。 该算法将基于流体策略的翻译；流体值函数近似值的替代；以及加强学习技术的应用。