RT Journal Article
T1 Application of decomposition techniques in a wildfire suppression optimization model
A1 Rodríguez Veiga, Jorge
A1 Rodríguez Penas, David
A1 González Rueda, Ángel Manuel
A1 Ginzo Villamayor, María José
K1 Assignment problems
K1 Wildfire management
K1 Decomposition techniques
K1 Benders decomposition
K1 Integer programming
AB Resource assignment and scheduling models provides an automatic and fast decision support system for wildfire suppression logistics. However, this process generates challenging optimization problems in many real-world cases, and the computational time becomes a critical issue, especially in realistic-size instances. Thus, to overcome that limitation, this work studies and applies a set of decomposition techniques such as augmented Lagrangian, branch and price, and Benders decomposition’s to a wildfire suppression model. Moreover, a reformulation strategy, inspired by Benders’ decomposition, is also introduced and demonstrated. Finally, a numerical study comparing the behavior of the proposals using different problem sizes is conducted
PB Springer
SN 2160-9543
YR 2023
FD 2023
LK http://hdl.handle.net/10347/30369
UL http://hdl.handle.net/10347/30369
LA eng
NO Rodríguez-Veiga, J., Penas, D.R., González-Rueda, Á.M. et al. Application of decomposition techniques in a wildfire suppression optimization model. Optim Eng (2023). https://doi.org/10.1007/s11081-022-09783-8
NO This research work is supported by the R+D+I project grants PID2020-116587GB-I00 and PID2021-124030NB (C31 and C32), funded by MCIN/AEI/10.13039/501100011033/ and by “ERDF A way of making Europe”/EU. Second author investigation is funded by the Xunta de Galicia (contract post-doctoral 2019-2022). We acknowledge the computational resources provided by CESGA. Third author acknowledges support from the Xunta de Galicia through the ERDF (ED431C-2020-14 and ED431G 2019/01), and “CITIC”
DS Minerva
RD 25 abr 2026