Wednesday, January 28, 2015
We are pleased to invite you to attend CREATE’s monthly research seminar!
WHEN: Wednesday, January 28 • 2:00 PM
LOCATION: RTH 306
(3710 McClintock Ave, Los Angeles, CA 90089)
SPEAKER: Amy Ward – Associate Professor of Data Sciences and Operations, Marshall School of Business at USC
TITLE: System Design under Uncertainty: Scheduling and Incentive Policies
ABSTRACT: We present several system design questions, and discuss their analysis. Our purpose is to understand which types of models are most relevant for the applications of interest to CREATE. We begin with a more classical network control problem, in which we must decide how to schedule jobs for processing at different stations in a network. Although there is a broad literature on such problems, there has been less focus on networks in which there is both sequential and parallel processing of jobs, and this is where our interest lies. Our next problem considers how to control congestion when processing resources are people, instead of machines, as is the case in many service systems. The difference is that machines work at fixed rates (as is assumed in traditional queueing theory) whereas people may work faster or slower, depending on their incentives. We want to understand how incentives influence system performance, and we do this by looking at a queueing game. We end by very briefly summarizing some current research thoughts on emergency department design, and on capacity sizing when there is demand uncertainty.
BIO: Amy Ward is an Associate Professor of Data Sciences and Operations in the Marshall School of Business at USC. She received her PhD from Stanford University in 2001, and then spent 4 years in the Industrial and Systems Engineering Department at Georgia Tech before coming to USC. She is an associate editor for Operations Research, Manufacturing & Service Operations Management, and Operations Research Letters. She is the vice-chair of the Applied Probability Society of INFORMS, and will become chair in 2016. Her research focuses on the approximation and control of stochastic systems, with applications to the manufacturing and service industries.