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MISTRAL: A game-theoretical model to allocate security measures in a multi-modal chemical transportation network with adaptive adversaries

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  • Talarico, Luca
  • Reniers, Genserik
  • Sörensen, Kenneth
  • Springael, Johan

Abstract

In this paper we present a multi-modal security-transportation model to allocate security resources within a chemical supply chain which is characterized by the use of different transport modes, each having their own security features. We consider security-related risks so as to take measures against terrorist acts which could target critical transportation systems. The idea of addressing security-related issues, by supporting decisions for preventing or mitigating intentional acts on transportation infrastructure, has gained attention in academic research only recently. The decision model presented in this paper is based on game theory and it can be employed to organize intelligence capabilities aimed at securing chemical supply chains. It enables detection and warning against impending attacks on transportation infrastructures and the subsequent adoption of security countermeasures. This is of extreme importance for preventing terrorist attacks and for avoiding (possibly huge) human and economic losses. In our work we also provide data sources and numerical simulations by applying the proposed model to a illustrative multi-modal chemical supply chain.

Suggested Citation

  • Talarico, Luca & Reniers, Genserik & Sörensen, Kenneth & Springael, Johan, 2015. "MISTRAL: A game-theoretical model to allocate security measures in a multi-modal chemical transportation network with adaptive adversaries," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 105-114.
    • Handle: RePEc:eee:reensy:v:138:y:2015:i:c:p:105-114
    DOI: 10.1016/j.ress.2015.01.022
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    Cited by:

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    2. Dong, Mingxin & Zhang, Zhen & Liu, Yi & Zhao, Dong Feng & Meng, Yifei & Shi, Jihao, 2023. "Playing Bayesian Stackelberg game model for optimizing the vulnerability level of security incident system in petrochemical plants," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    3. Lin, XuXun & Yuan, PengCheng, 2018. "A dynamic parking charge optimal control model under perspective of commuters’ evolutionary game behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1096-1110.
    4. Ditta, A. & Figueroa, O. & Galindo, G. & Yie-Pinedo, R., 2019. "A review on research in transportation of hazardous materials," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    5. Starita, Stefano & Scaparra, Maria Paola, 2016. "Optimizing dynamic investment decisions for railway systems protection," European Journal of Operational Research, Elsevier, vol. 248(2), pages 543-557.
    6. Zhang, Laobing & Reniers, Genserik & Qiu, Xiaogang, 2019. "Playing chemical plant protection game with distribution-free uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    7. Hunt, Kyle & Zhuang, Jun, 2024. "A review of attacker-defender games: Current state and paths forward," European Journal of Operational Research, Elsevier, vol. 313(2), pages 401-417.
    8. Laobing Zhang & Genserik Reniers, 2016. "A Game‐Theoretical Model to Improve Process Plant Protection from Terrorist Attacks," Risk Analysis, John Wiley & Sons, vol. 36(12), pages 2285-2297, December.
    9. Han, Lin & Zhao, Xudong & Chen, Zhilong & Wu, Yipeng & Su, Xiaochao & Zhang, Ning, 2021. "Optimal allocation of defensive resources to defend urban power networks against different types of attackers," International Journal of Critical Infrastructure Protection, Elsevier, vol. 35(C).
    10. Monroe, Jacob & Ramsey, Elizabeth & Berglund, Emily, 2018. "Allocating countermeasures to defend water distribution systems against terrorist attack," Reliability Engineering and System Safety, Elsevier, vol. 179(C), pages 37-51.

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