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Dynamic Resource Allocation in Disaster Response: Tradeoffs in Wildfire Suppression

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  • Nada Petrovic
  • David L Alderson
  • Jean M Carlson

Abstract

Challenges associated with the allocation of limited resources to mitigate the impact of natural disasters inspire fundamentally new theoretical questions for dynamic decision making in coupled human and natural systems. Wildfires are one of several types of disaster phenomena, including oil spills and disease epidemics, where (1) the disaster evolves on the same timescale as the response effort, and (2) delays in response can lead to increased disaster severity and thus greater demand for resources. We introduce a minimal stochastic process to represent wildfire progression that nonetheless accurately captures the heavy tailed statistical distribution of fire sizes observed in nature. We then couple this model for fire spread to a series of response models that isolate fundamental tradeoffs both in the strength and timing of response and also in division of limited resources across multiple competing suppression efforts. Using this framework, we compute optimal strategies for decision making scenarios that arise in fire response policy.

Suggested Citation

  • Nada Petrovic & David L Alderson & Jean M Carlson, 2012. "Dynamic Resource Allocation in Disaster Response: Tradeoffs in Wildfire Suppression," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-9, April.
    • Handle: RePEc:plo:pone00:0033285
    DOI: 10.1371/journal.pone.0033285
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    Cited by:

    1. Esther Jose & Puneet Agarwal & Jun Zhuang, 2023. "A data-driven analysis and optimization of the impact of prescribed fire programs on wildfire risk in different regions of the USA," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 181-207, August.
    2. Cameron MacKenzie & Hiba Baroud & Kash Barker, 2016. "Static and dynamic resource allocation models for recovery of interdependent systems: application to the Deepwater Horizon oil spill," Annals of Operations Research, Springer, vol. 236(1), pages 103-129, January.
    3. Eghbal Rashidi & Hugh Medal & Aaron Hoskins, 2018. "An attacker‐defender model for analyzing the vulnerability of initial attack in wildfire suppression," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(2), pages 120-134, March.
    4. Hongzhe Zhang & Xiaohang Zhao & Xiao Fang & Bintong Chen, 2024. "Proactive Resource Request for Disaster Response: A Deep Learning-Based Optimization Model," Information Systems Research, INFORMS, vol. 35(2), pages 528-550, June.
    5. Adam Behrendt & Vineet M. Payyappalli & Jun Zhuang, 2019. "Modeling the Cost Effectiveness of Fire Protection Resource Allocation in the United States: Models and a 1980–2014 Case Study," Risk Analysis, John Wiley & Sons, vol. 39(6), pages 1358-1381, June.
    6. Skorin-Kapov, Nina & Mesarić, Luka & García, Fernando Pereñíguez & Skorin-Kapov, Lea, 2024. "Scheduling aerial resource operations for the extinction of large-scale wildfires," Omega, Elsevier, vol. 122(C).
    7. Jude Bayham & Jonathan K. Yoder, 2020. "Resource Allocation under Fire," Land Economics, University of Wisconsin Press, vol. 96(1), pages 92-110.
    8. Cameron A. MacKenzie & Hiba Baroud & Kash Barker, 2016. "Static and dynamic resource allocation models for recovery of interdependent systems: application to the Deepwater Horizon oil spill," Annals of Operations Research, Springer, vol. 236(1), pages 103-129, January.

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