CAREER: Methodology for Optimization via Simulation: Bayesian Methods, Frequentist Guarantees, and Applications to Cardiovascular Medicine

职业：通过模拟进行优化的方法：贝叶斯方法、频率论保证以及在心血管医学中的应用

• 批准号: 1254298
• 负责人: Peter Frazier
• 金额: $ 40万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1254298&HistoricalAwards=false
• 关键词: CAREER; Methodology; Optimization; via; Simulation

The objective of this Faculty Early Career Development (CAREER) Program award is to develop improved methods for optimization via simulation, and to apply them to decision-making problems in cardiovascular medicine. Optimization via simulation is required whenever we wish to find the best among several options, and evaluating the quality of an option requires running a stochastic simulation. Two types of improved methods will be developed. First, methods for ranking and selection will be developed using a link between Bayesian average-case and frequentist worst-case performance. These methods promise to produce solutions more quickly than do existing methods, with frequentist statistical guarantees on solution quality that are tighter and whose form is more natural for engineering applications. Second, multistart gradient-based methods will be developed using Bayesian value of information analysis to efficiently allocate simulation effort across starts, allowing those starts more likely to have high-quality local optima to converge first. These methods promise to produce high-quality solutions more quickly than do existing approaches for allocating simulation effort across starts. The new methods will be demonstrated via application to two problems in cardiovascular medicine: the design of post-operative surveillance strategies for patients undergoing endovascular aneurysm repair; and the design and placement of grafts in patients undergoing bypass surgery.If successful, this research will both serve as an enabling technology within operations research, and will have specific benefits within cardiovascular medicine. Better post-operative surveillance will allow problems occurring after surgery to be detected more quickly, reducing the risk of aneurysm rupture. Better graft design will improve the reliability and longevity of surgical repairs, improving patient health. More broadly, as an enabling technology, the improved simulation optimization methods developed with this award will allow practitioners and researchers to solve a wider variety of optimization via simulation problems with greater speed and accuracy.

这个教师早期职业发展（CAREER）计划奖的目标是通过模拟开发优化的改进方法，并将其应用于心血管医学的决策问题。 每当我们希望在多个选项中找到最佳选项时，都需要通过模拟进行优化，并且评估选项的质量需要运行随机模拟。 将开发两种类型的改进方法。 首先，排名和选择的方法将开发使用贝叶斯平均情况和频率最坏情况下的性能之间的联系。 这些方法比现有的方法更快地产生解决方案，频率论的统计保证解决方案的质量更紧密，其形式更自然的工程应用。 其次，将使用贝叶斯信息分析值来开发基于多起点梯度的方法，以有效地在起点之间分配模拟工作，使那些起点更有可能首先收敛到高质量的局部最优值。 这些方法有望产生高质量的解决方案更快地比现有的方法分配模拟工作跨启动。 新方法将通过应用于心血管医学中的两个问题来证明：接受血管内动脉瘤修复术的患者的术后监测策略的设计;以及接受旁路手术的患者的移植物的设计和放置。如果成功，这项研究将成为手术研究中的一项使能技术，并将在心血管医学中产生特定的益处。 更好的术后监测将使手术后发生的问题能够更快地被发现，从而降低动脉瘤破裂的风险。 更好的移植物设计将提高手术修复的可靠性和寿命，改善患者健康。 更广泛地说，作为一项使能技术，通过该奖项开发的改进的仿真优化方法将使从业者和研究人员能够通过仿真问题以更快的速度和更高的准确性解决更广泛的优化问题。