Designing a Bioterrorism Response Network Considering Transportation: A Reinforcement Learning Approach

Authors

  • Mahdie Sadeghian Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran.
  • Fereshteh Parvaresh * Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran. https://orcid.org/0000-0001-8411-6307

https://doi.org/10.22105/raise.v3i2.88

Abstract

Bioterrorism attacks are among the most serious national security threats, requiring the design of rapid and effective response networks. This study presents a three-level hybrid model based on the Defender–Attacker–Defender (DAD) framework, in which strategic stockpiling, tactical distribution, and operational transportation decisions are addressed simultaneously. The novelty of this research lies in integrating the Vehicle Routing Problem (VRP) with capacity, time-window, and demand uncertainty considerations into the context of biological attacks. To overcome computational complexity and ensure real-time responsiveness to environmental changes, a Reinforcement Learning (RL) module is developed that learns optimal drug distribution routes through interaction with the environment, while balancing storage, transportation, and human loss costs. Numerical experiments and sensitivity analyses show that the proposed algorithm outperforms traditional methods by reducing both casualties and total costs, and it achieves fast convergence and high efficiency under uncertainty. Ultimately, this approach can serve as a foundation for developing real-scale response systems to biological crises.

Keywords:

Bioterrorism, Biological attacks, Vehicle routing problem, Crisis logistics, Mathematical modeling Reinforcement learning

References

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Published

2026-05-25

Issue

Vol. 3 No. 2 (2026): Research Annals of Industrial and Systems Engineering

Section

Articles

How to Cite

Sadeghian, M. ., & Parvaresh, F. . (2026). Designing a Bioterrorism Response Network Considering Transportation: A Reinforcement Learning Approach. Research Annals of Industrial and Systems Engineering, 3(2), 76–85. https://doi.org/10.22105/raise.v3i2.88

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