KDI: Synergistic and Decentralized Decision Making in Complex Stochastic Systems

KDI：复杂随机系统中的协同和分散决策

• 批准号: 9873339
• 负责人: Michael Caramanis
• 金额: $ 116.2万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-10-01 至 2003-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9873339&HistoricalAwards=false
• 关键词: KDI; Synergistic; Decentralized; Decision; Making

9873339CaramanisThe project will use the manufacturing environment as a paradigm for modeling and studying problems in distributed and hierarchical control under uncertainty. The primary objective of this project is to investigate and propose solutions to computational challenges that are the result of the proliferation of technologies which give rise to stochastic large-scale systems of unprecedented complexity. The second objective is to develop solutions and methodologies that not only meet the needs of the manufacturing supply chain coordination problem, but are general enough to be able to explore their impact on other areas of science and technology.The manufacturing enterprise is one consisting of multiple, but highly interconnected, hierarchical layers. The size, nonlinearity, and stochasticity of manufacturing systems renders effective, let alone optimal, centralized decision making intractable. The approach followed in the past has been to relate the system's state to a number of variables, e.g., randomness, decision vector, cost, and then solve for decoupled components in a decentralized decision framework. This produces tractable, but inefficient, decision rules because of the assumptions made to allow solutions.The goal of this research is to achieve systematic coordination of decentralized decision entities without compromising tractability of the problem. The issues involved span methodologies ranging from StochasticControl and Large Deviations Theory, to Discrete Event Dynamic Analysis, Neuro-dynamic Programming, and Concurrent Simulation.Since many of the techniques to be developed involve optimization and stochastic systems, the relevance of the methodological research on economics and statistical physics will be also examined.Strong ties with industry will allow deployment, implementation, and testing in an actual manufacturing environment to be included as part of the dissemination and transfer plan for the research approaches developed.

9873339Caramanis该项目将使用制造环境作为一个范例，用于建模和研究不确定性下的分布式和分层控制问题。 该项目的主要目标是研究和提出解决方案，以应对由于技术扩散而产生的前所未有的复杂性随机大规模系统的计算挑战。第二个目标是开发解决方案和方法，不仅满足制造业供应链协调问题的需要，而且具有足够的通用性，能够探索它们对其他科学和技术领域的影响。制造业企业是由多个但高度互连的分层结构组成的。 制造系统的规模、非线性和随机性使得有效的、更不用说最优的、集中的决策难以实现。过去所遵循的方法是将系统的状态与多个变量相关联，例如，随机性，决策向量，成本，然后在分散决策框架中求解解耦组件。 这产生了易处理的，但效率低下，决策规则，因为所作的假设，以允许solutions.This研究的目标是实现分散的决策实体的系统协调，而不损害问题的易处理性。 所涉及的问题涵盖了从随机控制和大偏差理论到离散事件动态分析、神经动态规划和并行模拟的方法论。由于许多要开发的技术涉及优化和随机系统，因此还将研究经济学和统计物理学方法论研究的相关性。与工业界的紧密联系将允许部署、实施、并在实际生产环境中进行测试，作为研究方法的传播和转让计划的一部分。