Risk-Stratified Multi-Objective Resource Allocation for Optimal Aviation Security Eva K. Lee 1,2,3 a , Taylor J. Leonard 2,4 b and Jerry C. Booker 5 1 The Data and Analytics Innovation Institute, Atlanta GA 30309, U.S.A. 2 Georgia Institute of Technology, Atlanta GA 30322, U.S.A. 3 Accuhealth Technologies, Atlanta GA 30310, U.S.A. 4 The United States Department of Air Force, Pentagon, Washington D.C. 20330, U.S.A. 5 The Transportation Security Administration, The United States Department of Homeland Security, U.S.A. Keywords: Data-Driven Enterprise Risk Assessment, Aviation Security, Transportation Security, Border Security, Security Measures, Multi-Objective Portfolio Optimization, Resource Allocation, Risk-Informed Decision, Mixed Integer Program. Abstract: This study aims to establish a quantitative construct for enterprise risk assessment and optimal portfolio investment to achieve the best aviation security. We first analyze and model various aviation transportation risks and establish their interdependencies via a topological overlap network. Next, a multi-objective portfolio investment model is formulated to optimally allocate security measures. The portfolio risk model determines the best security capabilities and resource allocation under a given budget. The computational framework allows for marginal cost analysis which determines how best to invest any additional resources for the best overall risk protection and return on investment. Our analysis involves cascading and inter-dependency modeling of the multi-tier risk taxonomy and overlaying security measures. The model incorporates three objectives: (1) maximize the risk posture (ability to mitigate risks) in aviation security, (2) minimize the probability of false clears, and (3) maximize the probability of threat detection. This work presents the first comprehensive model that links all resources across the 440 federally funded airports in the United States. We experimented with several computational strategies including Dantzig-Wolfe decomposition, column generation, particle swarm optimization, and a greedy heuristic to solve the resulting intractable instances. Contrasting the current baseline performance to some of the near-optimal solutions obtained by our system, our solutions offer improved risk posture, lower false clear, and higher threat detection across all the airports, indicating a better risk enterprise strategy and decision process under our system. The risk assessment and optimal portfolio investment construct are generalizable and can be readily applied to other risk and security problems. 1 INTRODUCTION In the aftermath of the September 11, 2001, terrorist attacks, the President of the United States signed the Aviation and Transportation Security Act into law requiring screening conducted by federal officials, 100 percent checked baggage screening, and expansion of the Federal Air Marshal Service and reinforced cockpit doors. The Transportation Security Administration (TSA) was subsequently created to oversee security in all modes of transportation. Specifically, a computer-assisted passenger pre- a https://orcid.org/0000-0003-0415-4640 b https://orcid.org/0000-0002-6753-9743 screening system, Computer-Assisted Passenger Pre- screening System (CAPPS) was developed to evaluate all passengers. The current generation, Secure Flight, is a risk-based passenger pre-screening program that matches passengers' names against trusted traveler lists and watchlists and categorizes them as high or low-risk (Administration, n.d.). Based on information derived from both government and commercial databases, Secure Flight conducts risk assessments to determine which passengers might be eligible for TSA precheck screening or standard screening. The results also prevent potential 104 Lee, E., Leonard, T. and Booker, J. Risk-Stratified Multi-Objective Resource Allocation for Optimal Aviation Security. DOI: 10.5220/0012769000003756 Paper published under CC license (CC BY-NC-ND 4.0) In Proceedings of the 13th International Conference on Data Science, Technology and Applications (DATA 2024), pages 104-117 ISBN: 978-989-758-707-8; ISSN: 2184-285X Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.