Publication:
Scheduling aerial resource operations for the extinction of large-scale wildfires

Simple item page
dc.contributor.authorSkorin-Kapov, Nina
dc.contributor.authorMesarić, Luka
dc.contributor.authorSkorin-Kapov, Lea
dc.contributor.authorPereñíguez García, Fernando
dc.contributor.departmentIngeniería de la Información y las Comunicaciones
dc.date.accessioned2026-03-02T10:59:39Z
dc.date.available2026-03-02T10:59:39Z
dc.date.copyright© 2023 The Author(s)
dc.date.issued2024
dc.description.abstractThe significant increase in large-scale wildfire events in recent decades, caused primarily by climate change, has resulted in a growing number of aerial resources being used in suppression efforts. Present-day management lacks efficient and scalable algorithms for complex aerial resource allocation and scheduling for the extinction of such fires, which is crucial to ensuring safety while maximizing the efficiency of operations. In this work, we present a Mixed Integer Linear Programming (MILP) optimization model tailored to large-scale wildfires for the daily scheduling of aerial operations. The main objective is to achieve a prioritized target water flow over all areas of operation and all time periods. Minimal target completion across individual areas and time periods and total water output are also maximized as secondary and ternary objectives, respectively. An efficient and scalable multi-start heuristic, combining a randomized greedy approach with simulated annealing employing large neighborhood search techniques, is proposed for larger instances. A diverse set of problem instances is generated with varying sizes and extinction strategies to test the approaches. Results indicate that the heuristic can achieve (near)-optimal solutions for smaller instances solvable by the MILP, and gives solutions approaching target water flows for larger problem sizes. The algorithm is parallelizable and has been shown to give promising results in a small number of iterations, making it applicable for both night-before planning and, more time-sensitive, early-morning scheduling.
dc.formatapplication/pdf
dc.format.extent19
dc.identifier.citationOmega, Volume 122, January 2024, 102941
dc.identifier.doihttps://doi.org/10.1016/j.omega.2023.102941
dc.identifier.eissn1873-5274
dc.identifier.issn0305-0483
dc.identifier.urihttp://hdl.handle.net/10201/216843
dc.languageeng
dc.publisherElsevier
dc.relationThis work was supported by Grant PID2020-112675RB-C41 funded by MCIN/AEI /10.13039/501100011033.
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0305048323001056
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAerial firefighting
dc.subjectScheduling
dc.subjectMixed integer linear programming model
dc.subjectRandomized greedy heuristic
dc.subjectSimulated annealing
dc.subject.odsNo relacionado con ningún objetivo de desarrollo sostenible
dc.titleScheduling aerial resource operations for the extinction of large-scale wildfires
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublicationes
relation.isAuthorOfPublication11194db1-33ae-4229-8840-6821d0de651e
relation.isAuthorOfPublication.latestForDiscovery11194db1-33ae-4229-8840-6821d0de651e
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
1-s2.0-S0305048323001056-main.pdf
Size:
884.03 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.37 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Artículos