IDEAS home Printed from https://ideas.repec.org/a/spr/operea/v25y2025i1d10.1007_s12351-024-00882-1.html
   My bibliography  Save this article

A mathematical programming approach for a wildfire suppression problem

Author

Listed:
  • Bibiana Granda

    (Interdisciplinary Mathematics Institute (IMI))

  • Begoña Vitoriano

    (Interdisciplinary Mathematics Institute (IMI))

  • José Rui Figueira

    (Universidade de Lisboa)

Abstract

Wildfires are natural recurrent events, that may be devastating if not addressed correctly. In these situations, where quick and accurate decisions are needed, Operational Research can be helpful for providing fast and robust solutions. This paper focuses on the response actions taken during the suppression stage of a wildfire. A mixed integer linear programming model is proposed to obtain a wildfire suppression strategy, including the wildfire behaviour changes induced by the solution. The selected wildfire suppression strategy is modelled in detail, pointing out which locations to control and their timing, based on available paths between them, avoiding engagement in dangerous situations. A computational study is carried out to determine the most suitable solver to provide exact solutions of the model. Also, a two-stage version of the model is proposed to deal with the multicriteria nature of the problem. A case study is also included to validate the model’s applicability, which is solved using the two proposed versions of the model and an iterative approach to compare their performance.

Suggested Citation

  • Bibiana Granda & Begoña Vitoriano & José Rui Figueira, 2025. "A mathematical programming approach for a wildfire suppression problem," Operational Research, Springer, vol. 25(1), pages 1-27, March.
    • Handle: RePEc:spr:operea:v:25:y:2025:i:1:d:10.1007_s12351-024-00882-1
    DOI: 10.1007/s12351-024-00882-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12351-024-00882-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12351-024-00882-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Marie Coffin & Matthew J. Saltzman, 2000. "Statistical Analysis of Computational Tests of Algorithms and Heuristics," INFORMS Journal on Computing, INFORMS, vol. 12(1), pages 24-44, February.
    2. Jude Bayham & Jonathan K. Yoder, 2020. "Resource Allocation under Fire," Land Economics, University of Wisconsin Press, vol. 96(1), pages 92-110.
    3. Marius M. Solomon, 1987. "Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints," Operations Research, INFORMS, vol. 35(2), pages 254-265, April.
    4. João António Zeferino, 2020. "Optimizing the location of aerial resources to combat wildfires: a case study of Portugal," 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. 100(3), pages 1195-1213, February.
    5. Zhongzhen Yang & Liquan Guo & Zaili Yang, 2019. "Emergency logistics for wildfire suppression based on forecasted disaster evolution," Annals of Operations Research, Springer, vol. 283(1), pages 917-937, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yi-Kuei Lin & Cheng-Fu Huang & Yi-Chieh Liao, 2019. "Reliability of a stochastic intermodal logistics network under spoilage and time considerations," Annals of Operations Research, Springer, vol. 277(1), pages 95-118, June.
    2. Zhang, Ying & Qi, Mingyao & Miao, Lixin & Liu, Erchao, 2014. "Hybrid metaheuristic solutions to inventory location routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 70(C), pages 305-323.
    3. Lu, Quan & Dessouky, Maged M., 2006. "A new insertion-based construction heuristic for solving the pickup and delivery problem with time windows," European Journal of Operational Research, Elsevier, vol. 175(2), pages 672-687, December.
    4. Babagolzadeh, Mahla & Zhang, Yahua & Abbasi, Babak & Shrestha, Anup & Zhang, Anming, 2022. "Promoting Australian regional airports with subsidy schemes: Optimised downstream logistics using vehicle routing problem," Transport Policy, Elsevier, vol. 128(C), pages 38-51.
    5. Cheng, Chun & Adulyasak, Yossiri & Rousseau, Louis-Martin, 2020. "Drone routing with energy function: Formulation and exact algorithm," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 364-387.
    6. Gutiérrez-Jarpa, Gabriel & Desaulniers, Guy & Laporte, Gilbert & Marianov, Vladimir, 2010. "A branch-and-price algorithm for the Vehicle Routing Problem with Deliveries, Selective Pickups and Time Windows," European Journal of Operational Research, Elsevier, vol. 206(2), pages 341-349, October.
    7. Ann-Kathrin Rothenbächer & Michael Drexl & Stefan Irnich, 2018. "Branch-and-Price-and-Cut for the Truck-and-Trailer Routing Problem with Time Windows," Transportation Science, INFORMS, vol. 52(5), pages 1174-1190, October.
    8. Hernandez, Florent & Feillet, Dominique & Giroudeau, Rodolphe & Naud, Olivier, 2016. "Branch-and-price algorithms for the solution of the multi-trip vehicle routing problem with time windows," European Journal of Operational Research, Elsevier, vol. 249(2), pages 551-559.
    9. Pradhananga, Rojee & Taniguchi, Eiichi & Yamada, Tadashi & Qureshi, Ali Gul, 2014. "Bi-objective decision support system for routing and scheduling of hazardous materials," Socio-Economic Planning Sciences, Elsevier, vol. 48(2), pages 135-148.
    10. Ehmke, Jan Fabian & Campbell, Ann M. & Thomas, Barrett W., 2018. "Optimizing for total costs in vehicle routing in urban areas," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 242-265.
    11. Luca Maria Gambardella & Marco Dorigo, 2000. "An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem," INFORMS Journal on Computing, INFORMS, vol. 12(3), pages 237-255, August.
    12. Gronalt, Manfred & Hartl, Richard F. & Reimann, Marc, 2003. "New savings based algorithms for time constrained pickup and delivery of full truckloads," European Journal of Operational Research, Elsevier, vol. 151(3), pages 520-535, December.
    13. Qi, Mingyao & Lin, Wei-Hua & Li, Nan & Miao, Lixin, 2012. "A spatiotemporal partitioning approach for large-scale vehicle routing problems with time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 248-257.
    14. Neves-Moreira, Fábio & Almada-Lobo, Bernardo & Guimarães, Luís & Amorim, Pedro, 2022. "The multi-product inventory-routing problem with pickups and deliveries: Mitigating fluctuating demand via rolling horizon heuristics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    15. Baldacci, Roberto & Mingozzi, Aristide & Roberti, Roberto, 2012. "Recent exact algorithms for solving the vehicle routing problem under capacity and time window constraints," European Journal of Operational Research, Elsevier, vol. 218(1), pages 1-6.
    16. Gitae Kim, 2024. "Electric Vehicle Routing Problem with States of Charging Stations," Sustainability, MDPI, vol. 16(8), pages 1-17, April.
    17. Calvete, Herminia I. & Gale, Carmen & Oliveros, Maria-Jose & Sanchez-Valverde, Belen, 2007. "A goal programming approach to vehicle routing problems with soft time windows," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1720-1733, March.
    18. Zhiping Zuo & Yanhui Li & Jing Fu & Jianlin Wu, 2019. "Human Resource Scheduling Model and Algorithm with Time Windows and Multi-Skill Constraints," Mathematics, MDPI, vol. 7(7), pages 1-18, July.
    19. Gréanne Leeftink & Erwin W. Hans, 2018. "Case mix classification and a benchmark set for surgery scheduling," Journal of Scheduling, Springer, vol. 21(1), pages 17-33, February.
    20. Liu, Fuh-Hwa Franklin & Shen, Sheng-Yuan, 1999. "A route-neighborhood-based metaheuristic for vehicle routing problem with time windows," European Journal of Operational Research, Elsevier, vol. 118(3), pages 485-504, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:operea:v:25:y:2025:i:1:d:10.1007_s12351-024-00882-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.