CAREER: Real-Time Stochastic Optimization with Large Structured Strategy Sets and High-Volume Data Streams

职业：具有大型结构化策略集和大容量数据流的实时随机优化

• 批准号: 0746844
• 负责人: Paat Rusmevichientong
• 金额: $ 40万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2011-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0746844&HistoricalAwards=false
• 关键词: CAREER; Real; Time; Stochastic; Optimization

This Faculty Early Career Devevelopment (CAREER)research proposes to support the development of a modeling and algorithmic framework for solving large-scale real-time stochastic optimization problems. This research will focus on a setting where distributions of the underlying uncertainty are not known. The decision maker in this environment often faces a large number of alternatives and must make a decision in real-time based on high-volume data streams. By exploiting specific structures of each problem, this research will develop methodologies that combine real-time decision-making with approximation algorithms for solving complex stochastic optimization problems having large strategy sets. The research will investigate applications of these algorithms to problems in search-based advertising and supply chain management by working with industry partners. In addition, the outcome of the research will be integrated into an interactive teaching module or a case study. The proposed research encompasses many problems in the current information-driven environment, including ad placement in search-based advertising services, inventory management for online retailers, and the multi-armed bandit problem. If successful, the research will result in a unifying framework for studying and analyzing this class of problems, along with a suite of algorithms applicable to these problems. The research will establish new techniques for extending the performance guarantee of approximation algorithms to reflect long-run average performance of the system, and provide new methodologies for improving the convergence rates by leveraging special structures within each problem. Extensive collaboration with companies in the industry and the integration of these relationships to enrich the classroom experience for students will be an important outcome of this project.

这项教师的早期职业发展（职业）研究提议支持开发建模和算法框架，以解决大规模的实时随机优化问题。 这项研究将重点放在未知的基本不确定性分布的环境上。 在这种环境中的决策者通常面临大量替代方案，必须基于大量数据流实时做出决定。 通过利用每个问题的特定结构，这项研究将开发方法，将实时决策与近似算法相结合，以解决具有较大策略集的复杂随机优化问题。 该研究将通过与行业合作伙伴合作，调查这些算法对基于搜索的广告和供应链管理问题的应用。 此外，研究结果将集成到互动教学模块或案例研究中。 拟议的研究涵盖了当前信息驱动的环境中的许多问题，包括基于搜索的广告服务中的广告放置，在线零售商的库存管理以及多军匪徒的库存问题。如果成功，这项研究将导致一个统一的框架来研究和分析此类问题，以及适用于这些问题的算法套件。 该研究将建立新技术，以扩展近似算法的性能保证，以反映系统的长期平均性能，并通过利用每个问题中的特殊结构来提高收敛速率。 与行业公司的广泛合作以及这些关系的整合以丰富学生的课堂体验将是该项目的重要结果。