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Wildland Fuel Treatment Planning Optimization Under Uncertainty

Lagos, Tomas (2023) Wildland Fuel Treatment Planning Optimization Under Uncertainty. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Wildfires present significant challenges with wide-ranging consequences for human lives, property, ecosystems, and economies. Fuel treatment, through vegetation removal to reduce fire potential and severity, is crucial for containing and controlling wildfires, offering benefits such as damage mitigation, cost reduction, minimized health impacts, and improved post-fire recovery. Prescribed burning and mechanical treatments have proven to be effective in practice, despite various challenges including budget constraints and limited treatment effectiveness in some settings.

This research highlights the need to consider uncertainty and multiple treatment types simultaneously in fuel treatment planning. The findings outlined in this thesis offer valuable insights and methodologies for optimizing fuel treatment planning and hence, supporting decision-makers in implementing robust planning solutions.

More specifically, this thesis introduces novel mathematical models and solution approaches to enhance fuel treatment decision-making.
It consists of three major parts.
The first part of the thesis explores uncertainty in fire occurrences and their locations by considering a conservative approach that can minimize several fire risk measures.
The second part ignores fire occurrences at all, and focuses on the fuel growth and the fuel treatment effect uncertainty. For the models in both of these parts we develop tailored solution approaches and explore their performance using real-life and semi-synthetic instances, providing practical insights for decision-makers. The superior performance of the proposed models across several meaningful objective functions is demonstrated. Finally, the third part of the thesis provides additional theoretical results on the computational complexity of strong-weak bilevel linear programs.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Lagos, Tomastomas.lagos.gonzalez@gmail.comtol280000-0003-1457-4665
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorProkopyev, Oleg A.oap@pitt.eduoap0000-0003-2888-8630
Committee ChairNtaimo, Lewisntaimo@tamu.edu0000-0002-9114-5170
Committee ChairRajgopal, Jayantj.rajgopal@pitt.edugunner10000-0001-7730-8749
Committee ChairOliver, Hinderohinder@pitt.eduohinder0000-0002-5077-0359
Date: 14 September 2023
Date Type: Publication
Defense Date: 12 July 2023
Approval Date: 14 September 2023
Submission Date: 24 July 2023
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 164
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Industrial Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: wildfires; fuel treatment planning; bilevel optimization; robust optimization
Date Deposited: 14 Sep 2023 13:38
Last Modified: 14 Sep 2023 13:38


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