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Balancing congestion and security in the presence of strategic applicants with private information

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  • Wang, Xiaofang
  • Zhuang, Jun

Abstract

Concerns on security and congestion appear in security screening which is used to identify and deter potential threats (e.g., attackers, terrorists, smugglers, spies) among normal applicants wishing to enter an organization, location, or facility. Generally, in-depth screening reduces the risk of being attacked, but creates delays that may deter normal applicants and thus, decrease the welfare of the approver (authority, manager, screener). In this paper, we develop a model to determine the optimal screening policy to maximize the reward from admitting normal applicants net of the penalty from admitting bad applicants. We use an M/M/1 queueing system to capture the impact of security screening policies on system congestion and use game theory to model strategic behavior, in which potential applicants with private information can decide whether to apply based on the observed approver's screening policy and the submission behavior of other potential applicants. We provide analytical solutions for the optimal non-discriminatory screening policy and numerical illustrations for both the discriminatory and non-discriminatory policies. In addition, we discuss more complex scenarios including imperfect screening, abandonment behavior of normal applicants, and non-zero waiting costs of attackers.

Suggested Citation

  • Wang, Xiaofang & Zhuang, Jun, 2011. "Balancing congestion and security in the presence of strategic applicants with private information," European Journal of Operational Research, Elsevier, vol. 212(1), pages 100-111, July.
  • Handle: RePEc:eee:ejores:v:212:y:2011:i:1:p:100-111
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Ni, Guanqun & Xu, Yinfeng & Dong, Yucheng, 2013. "Price and speed decisions in customer-intensive services with two classes of customers," European Journal of Operational Research, Elsevier, vol. 228(2), pages 427-436.
    2. Hausken, Kjell & Zhuang, Jun, 2013. "The impact of disaster on the strategic interaction between company and government," European Journal of Operational Research, Elsevier, vol. 225(2), pages 363-376.
    3. repec:spr:annopr:v:236:y:2016:i:1:d:10.1007_s10479-015-1810-z is not listed on IDEAS
    4. repec:eee:ejores:v:265:y:2018:i:3:p:919-930 is not listed on IDEAS
    5. Shan, Xiaojun & Zhuang, Jun, 2013. "Hybrid defensive resource allocations in the face of partially strategic attackers in a sequential defender–attacker game," European Journal of Operational Research, Elsevier, vol. 228(1), pages 262-272.
    6. repec:eee:reensy:v:165:y:2017:i:c:p:292-301 is not listed on IDEAS
    7. Liya Guo & Shan Huang & Jun Zhuang & Adel Sadek, 2013. "Modeling Parking Behavior Under Uncertainty: A Static Game Theoretic versus a Sequential Neo-additive Capacity Modeling Approach," Networks and Spatial Economics, Springer, vol. 13(3), pages 327-350, September.
    8. Konrad, Renata A. & Trapp, Andrew C. & Palmbach, Timothy M. & Blom, Jeffrey S., 2017. "Overcoming human trafficking via operations research and analytics: Opportunities for methods, models, and applications," European Journal of Operational Research, Elsevier, vol. 259(2), pages 733-745.
    9. repec:spr:annopr:v:258:y:2017:i:2:d:10.1007_s10479-015-2043-x is not listed on IDEAS
    10. Gallice, Andrea, 2017. "An approximate solution to rent-seeking contests with private information," European Journal of Operational Research, Elsevier, vol. 256(2), pages 673-684.

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