Description:

Abstract: Dynamic programming is a canonical tool for solving complex sequential decision problems in operations. Yet, because it suffers from the curse of dimensionality, one often must rely on approximations. Among these, deterministic—or “fluid”—approximations have long served as tractable benchmarks that reveal key structural properties of optimal or near-optimal policies in dynamic resource allocation problems across service operations and revenue management.

While such fluid approximations have been widely—and often ad hoc—applied, this talk presents a systematic framework for leveraging them to design high-quality control policies through what we call fluid lookahead. I will illustrate the approach in a family of finite-horizon revenue management problems, showing how fluid lookahead captures key structural properties of the true optimal policies and, perhaps surprisingly, achieves near-optimal performance with only a few lookahead steps. I will also discuss how the same principles extend naturally to infinite-horizon settings.

This is joint work with Daniel Loredo Duran (PhD student) and Jan A. Van Mieghem.

Bio: Itai Gurvich is a Professor at the Kellogg School of Management, Northwestern University. He earned his Ph.D. from Columbia University’s Graduate School of Business in 2008 and joined Kellogg the same year. From 2016 to 2020, he was on the faculty of Cornell University’s campus in New York City (Cornell Tech) before returning to Kellogg in 2021.

Professor Gurvich’s research focuses on the performance analysis and optimization of processing networks, as well as the theory of stochastic-process approximations. His work has been recognized with the INFORMS Applied Probability Society’s Best Publication Award. He has served as the Stochastic Models Area Editor for Operations Research and as Chair of the INFORMS Applied Probability Society.