Sensitivity Analysis and Real-Time Control of Discrete Event Systems

该项目的两个主要目标是：（a）建立一个 评估性能敏感性的一般框架 在线离散事件动态系统（DEDS），以及（B） 将灵敏度信息集成到优化算法中 具有实时适用性。 该研究计划将继续 一些新的方向，在样本路径为基础的方法，使用 高效构建的“增强系统”。“增强系统 旨在捕捉名义上的 和扰动DEDS，只使用直接观察到的信息。 对于广泛的模型和性能标准， 预计框架将提供敏感性估计， 关于可能是真实的参数（例如作业流），或 整数值（例如，在最佳动态 政策）。 将开发算法来处理 基本的DEDS控制问题，如动态路由， 调度和自适应资源共享。 敏感性分析 将被集成到静态和动态分布式算法中 用于解决存在的性能优化问题， 实时限制（deadline）。 的结果 预计项目将有助于不断努力， DEDS分析的一般方法。 他们还预计， 为在线优化提供可行的算法， 没有可用分析模型的复杂系统。 这项工作的结果是非常有希望的处理 新兴技术环境的控制问题， 自动化制造，分布式处理系统，以及 计算机网络。PI资质良好，机构 为研究提供了良好的环境。 三年 奖励是强烈推荐的。

The two major objectives of this project are (a) to establish a general framework for estimating performance sensitivities of discrete event dynamic systems (DEDS) on-line, and (b) to integrate sensitivity information into optimization algorithms with real-time applicability. The proposed research will pursue some new directions in sample-path-based methodologies, using efficiently constructed "augmented systems." Augmented systems are intended to capture the stochastic behavior of both a nominal and perturbed DEDS, using only information directly observed. For a broad class of models and performance criteria, this framework is expected to provide sensitivity estimates with respect to parameters which may be real (e.g. job flows) or integer-valued (e.g. thresholds used in optimal dynamic policies). Algorithms will be developed for dealing with fundamental DEDS control problems, such as dynamic routing, scheduling, and adaptive resource sharing. Sensitivity analysis will be integrated into static and dynamic distributed algorithms for addressing performance optimization problems in the presence of real-time constraints (deadlines). The results of this project are expected to contribute to the growing effort towards general methodologies for DEDS analysis. They are also expected to provide workable algorithms for on-line optimization of complex systems with no available analytical models. The results from this work are very promising for dealing with control issues for such emerging technological environments as automated manufacturing, distributed processing systems, and computer networks. The PI is well qualified and the institution provides an excellent environment for the research. A three-year award is highly recommended.