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Workshop on Decision and Control of Discrete Event Dynamic Systems

Workshop on Decision and Control of Discrete Event Dynamic Systems
离散事件动态系统决策与控制研讨会
批准号:
8721856
负责人:
Chelsea White
金额:
$2.86万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
1988
资助国家:
美国
项目状态:
已结题
起止时间:
1988-01-01 至 1988-12-31

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中文摘要
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英文摘要
Proposal is for a 3-day Workshop, to be attended by the major researchers in Discrete Event Dynamics Systems. It is impractical to obtain reviews from persons who are knowledgeable but uninvolved. The DEDS area has been gradually growing. Operations research problems in manufacturing systems have led to System Theory models (such as markov decision processes) and larger areas of application (communication protocols, e.g.). The work of several major figures in Operation Research and System Theory can now be compared and unified approaches synthesized. The goal of the Workshop, to provide future mission- oriented research applications, will give it focus; the rapidly developing field makes such a Workshop timely; and the Budget is realistic. In view of the probable future impact of the Workshop on DEDS research, I recommend awarding this proposal a grant in the amount requested.
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会议论文
Workshop on Supply Chain Security and Productivity: Research and Education Implications: January, 2004, Atlanta, GA
  • 批准号:
    0334492
  • 项目类别:
    Standard Grant
  • 资助金额:
    $3.0万
  • 财政年份:
    2003
  • 负责人:
    Chelsea White
  • 依托单位:
Heuristic Solution Approaches for Models of Sequential Decisionmaking Under Uncertainty and Risk
Heuristic Solution Approaches for Models of Sequential Decisionmaking Under Uncertainty and Risk
  • 批准号:
    8708183
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $10.59万
  • 财政年份:
    1987
  • 负责人:
    Chelsea White
  • 依托单位:
Knowledge Engineering Application to Data Network Design
  • 批准号:
    8608957
  • 项目类别:
    Standard Grant
  • 资助金额:
    $0.0万
  • 财政年份:
    1986
  • 负责人:
    Chelsea White
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis