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dc.contributor.authorKress, Moshe
dc.contributor.authorLin, Kyle Y.
dc.contributor.authorSzechtman, Roberto
dc.dateOctober 31, 2008
dc.date.accessioned2014-01-09T22:22:02Z
dc.date.available2014-01-09T22:22:02Z
dc.date.issued2008-10
dc.identifier.citation2009 “Scheduling Policies for an Antiterrorist Surveillance System”, (with K. Lin and R. Szechtman), Naval Research Logistics (NRL), V. 56, No. 2, pp 113-126.
dc.identifier.urihttp://hdl.handle.net/10945/38174
dc.descriptionNaval Research Logistics (NRL) V. 56, No. 2, pp 113-126.en_US
dc.description.abstractThis paper concerns scheduling policies in a surveillance system aimed at detecting a terrorist attack in time. Terrorist suspects arriving at a public area are subject to continuous monitoring, while a surveillance team takes their biometric signatures and compares them with records stored in a terrorist database. Because the surveillance team can screen only one terrorist suspect at a time, the team faces a dynamic scheduling problem among the suspects. We build a model consisting of an M/G/1 queue with two types of customers--red and white--to study this problem. Both types of customers are impatient, but the reneging time distributions are different. The server only receives a reward by serving a red customer, and can use the time a customer has spent in the queue to deduce its likely type. In a few special cases, a simple service rule--such as a first-come-first-serve--is optimal. We explain why the problem is in general different, and develop a heuristic policy motivation by the fact that terrorist attacks tend to be rare events.en_US
dc.rightsdefined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleScheduling Policies for an Antiterrorist Surveillance Systemen_US
dc.typeArticleen_US
dc.contributor.departmentOperations Research (OR)
dc.subject.authorhomeland securityen_US
dc.subject.authorcounterterrorismen_US
dc.subject.authormulticlass queueen_US
dc.subject.authorrenegingen_US
dc.subject.authordynamic schedulingen_US


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