Authors:
Eva Lee
1
;
2
;
3
;
Taylor Leonard
2
;
4
and
Jerry Booker
5
Affiliations:
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.
Keyword(s):
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.
(More)