Optimization of Jump Stochastic Systems

跳跃随机系统的优化

• 批准号: 9500746
• 负责人: Eugene Feinberg
• 金额: $ 14.7万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-06-01 至 1999-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9500746&HistoricalAwards=false
• 关键词: Optimization; Jump; Stochastic; Systems

9500746 Feinberg This research is focused on the optimization of continuous time jump stochastic systems. In particular, the research is aimed at developing new methods for analyzing stochastic syqtems: (1) study the structure of optimal control strategies, (2) develop algorithms to compute optimal strategies, and (3) develop structural and computational results for optimal strategies in production/inventory and telecommunications systems. The investigator is proposing a new method of analysis for jump stochastic systems. The potential for the research work is that it will provide a unified framework for the analysis of both the Continuous Time Markov Decision Processes with piecewise constant trajectories and Semi-Markov Decision Processes. By using the new framework and some other modeling and analysis techniques, including finite and infinite-dimensional linear programming, the research will pursue to develop structural results and efficient algorithms for the solution of continuous time problems, including problems with multiple criteria and constraints. The results of this project will provide analytical methods and computational tools for dynamic optimization of a wide range of natural and man-made systems whose behavior can be described by stochastic processes with piecewise constant trajectories. Knowledge of how to control dynamic stochastic systems beyond Markovian systems is in its infancy. This research will advance the frontier toward a better understanding and control of generalized Semi-Markov Processes. Stochastic models that can adjust based on real time data are needed. This work paves the way toward the realization of such a goal.

小行星9500746 本文研究了连续时间跳变随机系统的优化问题。 特别是，研究的目的是开发新的方法来分析随机系统：（1）研究最优控制策略的结构，（2）开发算法来计算最优策略，（3）开发生产/库存和电信系统中的最优策略的结构和计算结果。 研究者提出了一种新的跳变随机系统分析方法。 潜在的研究工作是，它将提供一个统一的框架，分析连续时间马尔可夫决策过程与分段常数轨迹和半马尔可夫决策过程。 通过使用新的框架和其他一些建模和分析技术，包括有限和无限维线性规划，研究将寻求开发结构化的结果和有效的算法来解决连续时间问题，包括多个标准和约束的问题。 该项目的成果将为各种自然和人造系统的动态优化提供分析方法和计算工具，这些系统的行为可以通过具有分段恒定轨迹的随机过程来描述。 知识如何控制动态随机系统超越马尔可夫系统是在其起步阶段。 这项研究将推进前沿更好地理解和控制广义半马尔可夫过程。 需要能够基于真实的时间数据进行调整的随机模型。 这项工作为实现这一目标铺平了道路。