A Sequential Stochastic Multilevel Passenger Screening Problem for Aviation Security

Designing effective aviation security systems has become a problem of national concern. Passenger screening is an important component of aviation security. Developing real-time passenger screening strategies can be quite challenging. This paper identifies a methodology that can be used in real-time to optimally assign passengers to aviation security resources for the Sequential Stochastic Multilevel Passenger Screening Problem (SSMPSP). In SSMPSP, there is a set of classes available for screening passengers, each of which corresponds to several device types for passenger screening. Passengers arrive sequentially, and a prescreening system determines the passengers’ perceived risk levels, which becomes known upon check-in. The objective of SSMPSP is to use the passengers’ perceived risk levels to determine the optimal policy for screening passengers that maximize the expected total security, subject to capacity and assignment constraints. SSMPSP is formulated as a Markov Decision Process, and an optimal policy is found using dynamic programming. The Sequential Stochastic Assignment Heuristic (SSAH) is introduced to assign passengers to classes in real-time. A condition is provided under which SSAH yields the optimal policy. The model is illustrated with an example that incorporates flight schedule and passenger volume data extracted from the Official Airline Guide. Analysis of the example shows that SSAH is robust in thoroughly screening the high-risk passengers. The models and analysis provided in this paper provide insight into real-time operations of passenger screening systems.