Jorge Sefair, Ph.D.
Assistant Professor, Industrial Engineering
Arizona State University
Abstract Title: Dynamic Workforce Allocation to Improve Airport Screening Operations
The Transportation Security Administration (TSA) was established in 2001 with the mission to “protect the nation’s transportation systems to ensure freedom of movement for people and commerce”. One of TSA’s main operations is to conduct screening at airports for all departing passengers and baggage prior to boarding their flights. Critical decisions for the efficient management of the screening operations include number of open screening lanes and number of Transportation Security Officers allocated to each screening checkpoint at any time. This talk describes a set of models aiming to support predictive and prescriptive analysis for TSA’s operational decision-making. The predictive component is a forecasting model that combines flight schedules, computer vision algorithms, and other business fundamentals, with historically observed throughput patterns to predict passenger volumes. The prescriptive component consists of an optimization model and a solution strategy for operational decisions based on the predicted demands. These decisions include system (re)configurations, staffing, and routing of officers across checkpoints, subject to operational aspects such that physical infrastructure capacity and target quality of service (e.g., maximum wait times). The model also embeds tractable data-driven approximations of queueing metrics describing the system performance (e.g., passengers processed), while enforcing desirable properties of the prediction functions such as concavity and continuity. The resulting predicting functions are accurate and simpler than the closed-form expressions from queueing theory, facilitating their use in mixed-integer programming models. We present a real-world case study in a US airport to demonstrate the efficacy of the proposed models. Although our models are developed within the airport security context, they are applicable to any multi-server queueing system with nonhomogeneous demands.
About Jorge Sefair, Ph.D.
Jorge A. Sefair is an Assistant Professor of Industrial Engineering in the School of Computing and Augmented Intelligence at Arizona State University, where he also is a Senior Global Futures Scientist at the Global Institute of Sustainability and Innovation. Dr. Sefair is also affiliate faculty at the Simon A. Levin Mathematical, Computational, and Modeling Sciences Center, the Center for Biodiversity Outcomes, and the Center for Spatial Reasoning & Policy Analytics. Dr. Sefair holds a PhD in Industrial and Systems Engineering from the University of Florida (2015), and an MSc in Industrial Engineering (2008), BSc in Industrial Engineering (2006), and BA in Economics (2005) from Universidad de los Andes (Colombia). His research interests include network optimization, multistage optimization, and integer programming. In particular, he is motivated by applications of operations research in environmental planning, public policy, and service systems. His research has been interdisciplinary, having published academic works with colleagues from a variety of fields, including civil engineering, public health, ecology, biology, and economics, in journals such as INFORMS Journal on Computing, IISE Transactions, Networks, Naval Research Logistics, and Omega. Dr. Sefair’s research has been funded by the National Science Foundation, the Office of Naval Research, the Department of Homeland Security, among other federal and private organizations. Dr. Sefair is the recipient of an NSF CAREER award in Operations Engineering (2021).
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Meeting ID: 975 8764 4164
Department of Industrial & Systems Engineering