Abstract
We consider the problem of buying and locating equipment for covering a (discretized) region. We propose two approaches, based on mathematical programming modelling and the epsilon-constraint method, that allow obtaining the efficient frontier of a bi-objective optimization problem. In the first approach, the objectives are maximizing coverage and minimizing cost. In the second approach, lexicographic optimization is used to incorporate additional objectives - maximizing double coverage and minimizing the maximum fire rate of spread of uncovered points. The latter objective comes from the specific application that motivated this work: wildfire detection. We present results from a case study in a portuguese landscape, as an example of the potential of optimization models and methods to support decision making in such a relevant field.
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Acknowledgements
This research was supported by FCT - Fundação para a Ciência e Tecnologia, within the scope of project “O3F - An Optimization Framework to reduce Forest Fire” - PCIF/GRF/0141/2019. We also acknowledge the Municipality of Baião and the Volunteer Firefighters of Baião and Sta. Marinha do Zêzere for the constant support and for accompanying in the technical and field visits to Baião.
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Alvelos, F., Moura, S., Vieira, A., Bento-Gonçalves, A. (2023). A Bi-objective Optimization Approach for Wildfire Detection. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14105. Springer, Cham. https://doi.org/10.1007/978-3-031-37108-0_27
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DOI: https://doi.org/10.1007/978-3-031-37108-0_27
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