Robust and Adaptive Optimization; a Tractable Approach to Optimization Under Uncertainty

鲁棒性和自适应优化；

• 批准号: 0556106
• 负责人: Dimitris Bertsimas
• 金额: $ 45万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0556106&HistoricalAwards=false
• 关键词: Robust; Adaptive; Optimization; Tractable; Approach

This grant provides funding a) for the development of robust and adaptive optimization methods for multi-period optimization problems under uncertainty, b) for applications of these methods to air traffic control in the United States in the presence of adverse weather, and c) applications in revenue management, pricing and transportation network design. The approach will utilize a broad array of methodologies including all aspects of mathematical optimization, simulation, control theory, dynamical systems and game theory. From an educational standpoint, the results of this grant will serve as components in teaching modules on stochastic optimization and also be included in a book on robust optimization that aspires to present a unified and modern introduction to this area.Multi-period optimization under uncertainty is a central problem in many engineering and management problems, such as engineering design, supply chain optimization, financial planning, air traffic control and revenue management. If successful, the results of this research will enable the development of new tractable and scalable approaches to solving optimization under uncertainty using data directly and through the publication of the book mentioned above will also affect the way optimization under uncertainty is taught in both undergraduate and graduate curricula. As a test case, the results of this research will be applied to controlling air traffic in the United States in the presence of adverse weather.

这笔赠款提供资金a）用于开发不确定性下多周期优化问题的鲁棒和自适应优化方法，B）用于在美国存在不利天气的情况下将这些方法应用于空中交通管制，以及c）在收入管理，定价和运输网络设计中的应用。该方法将利用广泛的方法，包括数学优化，模拟，控制理论，动力系统和博弈论的各个方面。 从教育的角度来看，这笔资助的结果将作为随机优化教学模块的组成部分，也将包含在一本关于鲁棒优化的书中，该书旨在对该领域进行统一和现代的介绍。不确定性下的多周期优化是许多工程和管理问题的中心问题，如工程设计，供应链优化，财务规划，空中交通管制和收益管理。 如果成功的话，这项研究的结果将使新的易于处理和可扩展的方法，以解决直接使用数据的不确定性下的优化，并通过上述书的出版也将影响在本科和研究生课程中教授的不确定性下的优化方式。作为一个测试案例，这项研究的结果将被应用于控制在美国的空中交通在不利天气的存在。