Using fluid approximations for designing service systems Abstract: Service systems tend to be very complicated to analyze exactly. This has led to a large literature aimed at developing good approximations of performance measures of interest, and then further using these for decision making, in particular, for capacity selection, routing customers etc. These approximations typically involve using asymptotic analysis by utilizing versions of laws of large numbers, via fluid limits, and/or central limit theorems, via diffusion limits. It is typically believed that fluid limits provide a crude approximation, which is refined by diffusion limits (which also tend to be more complicated). In this talk, we will demonstrate that in many settings, fluid limits can in fact provide extremely accurate prescriptions without the need for any further refinements. Given the simplicity of computing fluid limits, this allows for much easier decision making. Focusing on a call center application, we will demonstrate how these fluid limits may be used for capacity selection and customer routing. Bio: Prof. Ramandeep Randhawa is an Associate Professor in the Information and Operations Management department in the Marshall School of Business at the University of Southern California. He received his Ph.D. in Operations, Information, and Technology from Stanford’s Graduate School of Business and has taught at the McCombs School of Business at the University of Texas prior to joining the Marshall School in 2009. Prof. Randhawa is an operations management scholar who studies capacity and flexibility, central issues in the field that are particularly important in service industries. One of his research streams examines production flexibility, such as the decision whether to have call center workers specialized to a single task versus trained to handle multiple tasks. Prof. Randhawa’s research shows among other things that only a small amount of flexibility is necessary to achieve efficiency. Another notable stream of research considers queuing problems of the sort that appear in theme parks and Netflix. He develops new algorithms for optimal queuing levels, developing methods that allow complex problems to be reduced to simpler problems, and shows that optimal capacity is often lower than previously believed. Prof. Randhawa’s work has been published in journals that include Management Science, Manufacturing and Service Operations Management, and Operations Research. He has received the Dean’s Award for Research Excellence in 2012 and his research has also been honored by the INFORMS Junior Faculty Interest Group. He currently serves as an Associate Editor for the journals Operations Research and Production and Operations Management.