Journal of Transportation Security

, Volume 6, Issue 2, pp 103–116 | Cite as

Optimal allocation of aviation security screening devices

  • Edward C. Sewell
  • Adrian J. Lee
  • Sheldon H. Jacobson
Article

Abstract

Recent advances in aviation security screening technologies have presented a new challenge in optimally allocating these devices across a set of airports. Following check-in at the ticket counter, self-service kiosk, or airline website application, each passenger is assigned to a security screening class through an automated passenger prescreening system based on their measured perceived risk level. The class to which a passenger is assigned can be used to determine how this passenger’s baggage will be screened by a set of security devices and procedures. In this paper, an explosive screening device allocation model is formulated as a nonlinear integer program to assign both the type of and number of devices to each class at each airport such that the total security is maximized, given a set of budget, resource, and passenger throughput constraints. A Dantzig-Wolfe decomposition approach is used to transform the nonlinear program into a binary integer program by redefining the binding constraint associated with the number of individual devices allocated across all airports. Computational results are provided for several randomly generated problems to demonstrate that the resulting binary integer program can be quickly solved to optimality.

Keywords

Aviation security Reliability problem Nonlinear integer program Dantzig-Wolfe decomposition 

Notes

Acknowledgments

The authors thank John J. Nestor of the Transportation Security Administration for helpful advice and feedback on the authors’ research in this area. This research was supported in part by the U.S. Air Force Office of Scientific Research [FA9550-10-1-0387] and the National Science Foundation [CMMI-0900226]. This research is based upon work supported in part by (while the third author served at) the National Science Foundation. The views expressed in this paper are those of the authors and do not reflect the official policy or position of the U.S. Air Force or Department of Defense, the National Science Foundation, or the U.S. government. The computational work was done in the Simulation and Optimization Laboratory housed within the Department of Computer Science at the University of Illinois at Urbana–Champaign.

References

  1. Butler V, Poole RW Jr (2002) Rethinking checked-baggage screening. Policy study 297. Reason Public Policy Institute, Los AngelesGoogle Scholar
  2. Cavusoglu H, Koh B, Raghunathan S (2010) An analysis of the impact of passenger profiling for transportation security. Oper Res 5(58):1287–1302CrossRefGoogle Scholar
  3. Feng Q, Sahin H, Kapur KC (2009) Designing airport checked-baggage-screening strategies considering system capability and reliability. Reliab Eng Syst Saf 94(2):618–627CrossRefGoogle Scholar
  4. Hawley K testimony (November 15, 2007) before the United States House of Representatives Committee on Oversight and Government Reform, http://www.tsa.gov/assets/pdf/111507_hogr_hawley_testimony.pdf
  5. Jacobson SH, Karnani T, Kobza JE, Ritchie L (2006) A cost-benefit analysis of alternative device configurations for aviation checked baggage security screening. Risk Anal 26(2):297–310CrossRefGoogle Scholar
  6. Kobza JE, Jacobson SH (1996) Addressing the dependency problem in access security system architecture design. Risk Anal 16(6):801–812CrossRefGoogle Scholar
  7. Kobza JE, Jacobson SH (1997) Probability models for access security system architectures. J Oper Res Soc 48(3):255–263Google Scholar
  8. Lazar Babu VL (2003) Airport security system design. Buffalo: Univ. at Buffalo (SUNY). ThesisGoogle Scholar
  9. McLay LA, Jacobson SH, Kobza JE (2006) A multilevel passenger screening problem for aviation security. Nav Res Logist 53(3):183–197CrossRefGoogle Scholar
  10. McLay LA, Jacobson SH, Kobza JE (2007) Integer programming models and analysis for a multilevel passenger screening problem. IIE Trans 391:73–81CrossRefGoogle Scholar
  11. McLay LA, Jacobson SH, Kobza JE (2008) The tradeoff between technology and prescreening intelligence in checked baggage screening for aviation security. J Transp Secur 1(2):107–126CrossRefGoogle Scholar
  12. McLay LA, Jacobson SH, Nikolaev AG (2009) A sequential stochastic passenger prescreening problem for aviation security. IIE Trans 41(6):575–591CrossRefGoogle Scholar
  13. McLay LA, Lee AJ, Jacobson SH (2010) Risk-based policies for airport security checkpoint screening. Transp Sci 44(3):333–349CrossRefGoogle Scholar
  14. Sewell EC, Attagara J, Kobza JE, Jacobson SH (2012) Allocating explosive screening devices for aviation security. J Transp Secur 5:141–155CrossRefGoogle Scholar
  15. Transportation Security Administration (2012a) 3-1-1 for carry-ons. Available at http://www.tsa.gov/311/. Accessed June 20, 2012
  16. Transportation Security Administration (2012b) TSA Implements First Milestone in Screening All Cargo on Passenger Planes. Available at http://www.tsa.gov/what_we_do/layers/aircargo/milestone.shtm. Accessed June 20, 2012
  17. United States Government Accountability Office (2003) Progress since September 11, 2001, and the challenges ahead. Technical Report GAO-03-1150T, Washington, DCGoogle Scholar
  18. Virta JE, Jacobson SH, Kobza JE (2003) Analyzing the cost of screening selectee and non-selectee baggage. Risk Anal 23(5):897–908CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Edward C. Sewell
    • 1
  • Adrian J. Lee
    • 2
  • Sheldon H. Jacobson
    • 3
  1. 1.Department of Mathematics & StatisticsSouthern Illinois University EdwardsvilleEdwardsvilleUSA
  2. 2.Central Illinois Technology and Education Research InstituteSpringfieldUSA
  3. 3.Department of Computer ScienceUniversity of IllinoisUrbanaUSA

Personalised recommendations