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A Simulation-Based Optimization Approach to the Firefighting Resource Scheduling Problem

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Computational Science and Its Applications – ICCSA 2024 Workshops (ICCSA 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14816))

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Abstract

In recent years, the number of forest fires has increased significantly. The main factors behind these disasters are rising temperatures and population growth. Optimization and simulation have been widely applied to forest firefighting problems, making it possible to improve the effectiveness and speed of firefighting actions. This work presents a forest firefighting resource scheduling problem, where a single firefighting resource is fighting 10 ignitions. A Genetic Algorithm (GA) is used to find the near-optimal sequence of actions, taking into account the maximization of the total unburned area. The solution found by the GA is evaluated using a Discrete-Event Simulation model developed in FlexSim software, thus validating the solution. Then, a simulation-based optimization approach is developed, involving uncertainty in some parameters.

This work has been supported by FCT Fundação para a Ciência e Tecnologia within the R &D Units Project Scope UIDB/00319/2020 and GEQProd (Grupo de Estudos em Qualidade e Produtividade), and the PhD grant reference UI/BD/150936/2021.

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Acknowledgments

The authors would like to thank FlexSim Software Products, Inc. for supporting this research by providing a provisional educational license for the software, version 24.0.2.

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Authors and Affiliations

  1. ALGORITMI Research Centre/LASI, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal

    Emerson J. Paiva, Marina A. Matos & Ana Maria A. C. Rocha

  2. Federal University of Itajubá, Campus Itabira, Itabira, Brazil

    Emerson J. Paiva

Authors
  1. Emerson J. Paiva
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  2. Marina A. Matos
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  3. Ana Maria A. C. Rocha
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Corresponding author

Correspondence to Ana Maria A. C. Rocha .

Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. School of Engineering, University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Department of Civil and Environmental Engineering and Architecture, University of Cagliari, Cagliari, Italy

    Chiara Garau

  4. Faculty of Information Technology, Clayton Campus, Monash University, Clayton, VIC, Australia

    David Taniar

  5. Algoritmi Research Centre, University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  6. Department of Chemistry, Biology and Biotechnology, University of Perugia, Perugia, Italy

    Maria Noelia Faginas Lago

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Paiva, E.J., Matos, M.A., Rocha, A.M.A.C. (2024). A Simulation-Based Optimization Approach to the Firefighting Resource Scheduling Problem. In: Gervasi, O., Murgante, B., Garau, C., Taniar, D., C. Rocha, A.M.A., Faginas Lago, M.N. (eds) Computational Science and Its Applications – ICCSA 2024 Workshops. ICCSA 2024. Lecture Notes in Computer Science, vol 14816. Springer, Cham. https://doi.org/10.1007/978-3-031-65223-3_26

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  • DOI: https://doi.org/10.1007/978-3-031-65223-3_26

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  • Online ISBN: 978-3-031-65223-3

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