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An Approximate Dynamic Program for Allocating Federal Air Marshals in Near Real-Time Under Uncertainty

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dc.contributor.advisor Ganesan, Rajesh
dc.contributor.author DeGregory, Keith W
dc.creator DeGregory, Keith W
dc.date 2014-04-30
dc.date.accessioned 2014-10-07T14:47:54Z
dc.date.available 2019-04-30T06:38:18Z
dc.date.issued 2014-10-07
dc.identifier.uri http://hdl.handle.net/1920/9012
dc.description.abstract The Federal Air Marshal Service provides front-line security in homeland defense by protecting civil aviation from potential terrorist attacks. Unique challenges arise in maximizing effective deployment of a limited number of air marshals to cover the risk posed by potential terrorists on nearly 30,000 daily domestic and international flights. Some risk presents in a stochastic nature (e.g., a last minute ticket sale where suspicion is aroused). Pre-scheduled air marshal deployments cannot respond to risk which presents stochastically in real-time. This dissertation proposes the formation of a quick reaction force to explicitly address stochastic risk of terrorism on commercial flights and presents a method for near real-time force allocation to optimize risk coverage. The dynamic allocation of reactionary air marshals requires sequential decision making under uncertainty with limited lead time. This dissertation investigates the application of an approximate dynamic program (ADP) to assist schedulers allocating air marshals in near real-time. ADP is a form of reinforced learning that seeks optimal decisions by incorporating future impacts rather than optimizing only on short-term rewards. The marshal allocation system is modeled as a Markov decision process. Due to the many variables and environment complexity, explicit storage of all states and their values is not possible. Value function approximation schemes are explored to mitigate scalability challenges by alleviating the need for state value storage. The study demonstrates that air marshal allocation in near real-time is possible using an ADP with value function approximation and results in improved coverage of stochastic risk over the myopic approach or pre-scheduling. en_US
dc.language.iso en en_US
dc.subject diffusion wavelet en_US
dc.subject approximate dynamic programming en_US
dc.subject value function approximation en_US
dc.subject sequential decision making under uncertainty en_US
dc.title An Approximate Dynamic Program for Allocating Federal Air Marshals in Near Real-Time Under Uncertainty en_US
dc.type Dissertation en
dc.description.note This dissertation has been embargoed for 5 years. It will not be available until April 30, 2019. en_US
thesis.degree.name Doctor of Philosophy in Systems Engineering and Operations Research en_US
thesis.degree.level Doctoral en
thesis.degree.discipline Systems Engineering and Operations Research en
thesis.degree.grantor George Mason University en


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