Staying Ahead of the Game: Adaptive Robust Optimization for
Dynamic Allocation of Threat Screening Resources
Abstract
We consider the problem of dynamically allocating screening resources of different efficacies (e.g.,
magnetic or X-ray imaging) at checkpoints (e.g., at
airports or ports) to successfully avert an attack by
one of the screenees. Previously, the Threat Screening Game model was introduced to address this
problem under the assumption that screenee arrival
times are perfectly known. In reality, arrival times
are uncertain, which severely impedes the implementability and performance of this approach. We
thus propose a novel framework for dynamic allocation of threat screening resources that explicitly accounts for uncertainty in the screenee arrival
times. We model the problem as a multistage robust
optimization problem and propose a tractable solution approach using compact linear decision rules
combined with robust reformulation and constraint
randomization. We perform extensive numerical
experiments which showcase that our approach outperforms (a) exact solution methods in terms of
tractability, while incurring only a very minor loss
in optimality, and (b) methods that ignore uncertainty in terms of both feasibility and optimality