Faculty & Research

Yuan Zhong

Assistant Professor of Operations Management

Phone :
1-773-702-3313
Address :
5807 South Woodlawn Avenue
Chicago, IL 60637

Yuan Zhong researches applied probability, as well as modeling and analysis of large-scale stochastic systems, with business and engineering applications. More recently his research interests have extended to applications in data centers and cloud computing. Zhong’s research has been published in several journals including Queueing Systems and The Annals of Applied Probability.

Prior to Booth, Zhong was an assistant professor at Columbia University in the Department of Industrial Engineering and Operations Research. At Columbia he taught courses in stochastic networks, simulation, and probability models. Before joining Columbia in 2013, Zhong spent one year as a Postdoctoral Scholar in the Computer Science Department at UC Berkeley.

Zhong received a PhD in operations research from MIT in2012, an MA in mathematics from Caltech in 2008, and a BA in mathematics from the University of Cambridge in 2006.

 

2016 - 2017 Course Schedule

Number Name Quarter
36600 Workshop in Operations/Management Science 2017 (Spring)
40000 Operations Management: Business Process Fundamentals 2017 (Spring)
40908 Special Topics in Operations Management 2017 (Summer)

2017 - 2018 Course Schedule

Number Name Quarter
36600 Workshop in Operations/Management Science 2017 (Fall)
36600 Workshop in Operations/Management Science 2018 (Spring)
40000 Operations Management: Business Process Fundamentals 2018 (Winter)
40907 Healthcare Operations 2018 (Spring)

REVISION: Process Flexibility for Multi-Period Production Systems
Date Posted: Jul  08, 2017
We develop a theory for the design of process flexibility in a multi-period production system. We propose and formalize a notion of "effective chaining" termed the Generalized Chaining Condition (GCC), which includes the chaining structure put forth by cite {JG95} as a special case. We show that any partial flexibility structure that satisfies GCC is near-optimal under a class of policies called the Max-Weight policies, i.e., gaining nearly as much benefit as a fully flexible system. Furthermore, we show that GCC can be satisfied using very sparse flexibility structures, and we provide an efficient algorithm for finding such structures. Our numerical study confirms insights from our theoretical results. The goal of this paper is to make progress towards the better understanding of the key design principles of process flexibility structures in a multi-period environment, the study of which has been limited due to its inherent complexity.