Various mathematical methods and metaheuristic approaches have been developed in the past to address optimization problems related to aviation security. One such problem deals with a key component of an aviation security system, baggage and passenger screening devices. The decision process to determine which devices to procure by aviation and security officials, and how and where to deploy them can be quite challenging. In this study, two evolutionary algorithms are developed to obtain optimal baggage screening strategies, which minimize the expected annual total cost. Here, the expected annual cost function is composed of the purchasing and operating costs, as well as the costs associated to false alarms and false clears. A baggage screening strategy consists of various hierarchical levels of security screening devices through which a checked bag may pass through. A solution to the aviation baggage screening problem entails the number and type of devices to be installed at each hierarchical level. Solutions obtained from a comparison of a Genetic and a Memetic algorithm are presented. In addition, to illustrate the performance of both algorithms, different computational experiments utilizing the developed algorithms are also presented.
Finding a needle in a haystack: Toward a psychologically informed method for aviation security screening.