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.
