A sequential stochastic passenger screening problem for aviation security

Designing effective aviation security systems has become a problem of national concern. Since September 11th, 2001 passenger screening systems have become an important component in the design and operation of aviation security systems. This paper introduces the Sequential Stochastic Passenger Screening Problem (SSPSP), which allows passengers to be optimally assigned (in real-time) to aviation security resources. Passengers are classified as either selectees or non-selectees, with screening procedures in place for each such class. Passengers arrive sequentially, and a prescreening system determines each passenger's perceived risk level, which becomes known upon check-in. The objective of SSPSP is to use the passengers' perceived risk levels to determine the optimal policy for screening passengers that maximizes the expected number of true alarms, subject to capacity and assignment constraints. SSPSP is formulated as a Markov decision process, and an optimal policy is found using dynamic programming. Several structural properties of SSPSP are derived using its relationship to knapsack and sequential assignment problems. An illustrative example is provided, which indicates that a large proportion of high-risk passengers are classified as selectees.