Collaborative Research: Risk-Averse Stochastic Optimization

合作研究：风险规避随机优化

• 批准号: 0354678
• 负责人: Andrzej Ruszczynski
• 金额: $ 16.91万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-15 至 2007-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0354678&HistoricalAwards=false
• 关键词: Collaborative; Research; Risk; Averse; Stochastic

This grant provides funding for the development of conceptually new models and solution methods for optimization of stochastic systems under high uncertainty and risk. The research will be based on the idea of integrating the theory of risk, stochastic optimization, and cutting-edge numerical techniques. It will focus on novel optimization models involving stochastic dominance constraints and risk measures in the objectives and in the constraints. This approach will capture the entire distribution of outcomes, including events of small probability but high consequences, rather than just the average performance. New solution methods will be developed that exploit the structure of the models and their probabilistic character. Models and methods for dynamic decision problems, which arise in many applications, will constitute a significant component of the project.The project has a high potential of providing a breakthrough in decision problems under high uncertainty and risk. If successful, it will provide decision-makers and analysts in many areas with novel tools for risk control. The results of the research will provide a qualitative advance in economics, finance, supply-chain management, manufacturing, military problems, telecommunication, electricity generation, and distribution, and many other areas involving high uncertainty and risk.

该补助金提供资金，用于开发概念上的新模型和解决方案，以优化高不确定性和风险下的随机系统。该研究将基于整合风险理论，随机优化和尖端数值技术的想法。 它将集中在新的优化模型，涉及随机优势约束和风险措施的目标和约束。这种方法将捕捉结果的整个分布，包括小概率但后果严重的事件，而不仅仅是平均表现。 将开发新的解决方案的方法，利用模型的结构和它们的概率特性。动态决策问题的模型和方法将是本项目的重要组成部分，它将为高不确定性和高风险下的决策问题提供突破性的研究。如果成功，它将为许多领域的决策者和分析师提供新的风险控制工具。 研究结果将在经济、金融、供应链管理、制造、军事问题、电信、发电和配电以及许多其他涉及高不确定性和风险的领域提供质的进步。