Department of Industrial Engineering & Decision Analytics [IEDA Seminar] - Singular Control of RBM in an Orthant: A Computational Method Based on Neural Networks

10:30am - 11:30am
Venue changed to Room 2405 (lift 17-18)

Motivated by applications in queueing theory, we consider a class of singular stochastic control problems whose state space is the d-dimensional positive orthant. The original problem is approximated by a drift control problem, to which we apply a recently developed computational method that is feasible for dimensions up to d=30 or more. To show that nearly optimal solutions are obtainable using this method, we present computational results for a variety of examples, including queueing network examples that have appeared previously in the literature.

This is joint work with Mike Harrison and Nian Si.

Event Format
Speakers / Performers:
Prof. Baris Ata
The University of Chicago, Booth School of Business

Baris Ata is the Sigmund E. Edelstone Distinguished Service Professor of Operations Management at The University of Chicago, Booth School of Business. He received the B.S. degree in Industrial Engineering from Bilkent University (TURKEY) in 1997, and MS degrees in Engineering-Economic Systems and Operations Research (1999), Business Research (2000), Mathematics (2001), and Statistics (2002) and a Ph.D. in Operations, Information, and Technology (2003) from Stanford Business School.

His current research interests include solving sequential stochastic decision problems in high dimensions with applications to stochastic networks, learning problems, public sector OR, revenue management and healthcare management. His research has been recognized by the Best Paper in Service Science Award, INFORMS (2009), William Pierskalla Best Paper Award, INFORMS (2015) and Wickham Skinner Best Paper Award, POMS (2019). He is also a recipient of the Manufacturing and Service Operations Management Young Scholar Prize, INFORMS (2015) and Emory Williams (schoolwide) MBA Teaching Award at Chicago Booth (2021). 

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Department of Industrial Engineering & Decision Analytics
Department of Information Systems, Business Statistics & Operations Management
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