Developing Decision Models for More Efficient Security Screenings

ISE associate professor Jun Zhuang has been awarded a three-year grant of $306K from the National Science Foundation for research on a project ''Robust Approval Process in the Face of Strategic Adversaries and Normal Applicants.'' 

Interestingly, this research was partially motivated by the fact that Zhuang was stuck in China for 100+ days in Spring 2009, due to a special "Security Advisory Opinion" process on his U.S. H1-b Visa application during his vacation trip to China in December 2008. During that frustrating period, Zhuang utilized his knowledge of game theory and queue theory to start this line of research, and published a paper with his colleague X. Wang, "Balancing Congestion and Security in the Presence of Strategic Applicants with Private Information," European Journal of Operational Research, 212(1): 100-111, 2011. 

The objective of this NSF project is to explore a new class of decision models to provide structural insights for robust screening when dealing with adaptive applicants and incomplete information. This research is motivated by public concerns on balancing congestion and safety due to security screening. Such screening has been used to identify and deter potential threats (e.g., terrorists, attackers, smugglers, spies) among normal applicants wishing to enter an organization, location, or facility. In-depth screening could reduce the risk of being attacked. However it may also create delays and deter normal applicants, which decreases the welfare of both the approver (authority, manager, screener) and the normal applicants. This research will consider the factors of security, congestion, equity, and the strategic and non-strategic responses from various applicant types. In particular, this research will study the applicants' strategies of applying, reneging, learning, and deceiving. This research will also study the approver's strategies of screening, dynamic service rates, multiple-servers and priority processing, multi-layer screening, and secrecy and deception. 

If successful, this research will lead to new frameworks that decision makers can use for screening diverse groups of strategic applicants. These new frameworks have the potential to reduce costs, avoid unnecessary waiting and inconvenience, and improve effectiveness and efficiency of the approval processes. Potential applications of this research include immigration systems, job market background checks, and airport/container/border controls. The relevance is illustrated by the recent national debate on selective "pat-downs" and "advanced imaging" screening, and the associated changing travel patterns. This research will engage many graduate, undergraduate, and high school students, including those from under-represented groups. The results of this research will be disseminated broadly to local, national and international communities.

UB ISE graduate students (John Coles, Cen Song, and Jie Xu) and undergraduate students (Kristen Alcazaren, Marie Catalano, Elizabeth Newell, and Paige Tesmer) have been working on this project, presented at research conferences, and received multiple internal and external research awards.

For more information, please contact Dr. Jun Zhuang