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

该学院的早期职业发展（CAREER）研究旨在支持解决大规模实时随机优化问题的建模和算法框架的开发。 这项研究将集中在一个设置的基本不确定性的分布是未知的。 在这种环境中，决策者往往面临大量的选择，必须根据大量的数据流实时做出决策。 通过利用每个问题的特定结构，本研究将开发方法，结合联合收割机实时决策与近似算法，解决复杂的随机优化问题，具有大的战略集。 该研究将通过与行业合作伙伴合作，调查这些算法在基于搜索的广告和供应链管理中的应用。 此外，研究成果将被纳入互动教学模块或案例研究。 拟议的研究包括许多问题，在当前的信息驱动的环境中，包括广告投放在基于搜索的广告服务，库存管理的在线零售商，和多臂强盗问题。如果成功的话，这项研究将为研究和分析这类问题提供一个统一的框架，沿着一套适用于这些问题的算法。 该研究将建立新的技术，用于扩展近似算法的性能保证，以反映系统的长期平均性能，并提供新的方法，通过利用每个问题中的特殊结构来提高收敛速度。 与业内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，将是该项目的重要成果。

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.