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Locating Emergency Vehicles: Robust Optimization Approaches

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12251))

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Abstract

The location of emergency vehicles is crucial for guaranteeing that populations have access to emergency services and that the provided care is adequate. These location decisions can have an important impact on the mortality and morbidity resulting from emergency episodes occurrence. In this work two robust optimization models are described, that explicitly consider the uncertainty that is inherent in these problems, since it is not possible to know in advance how many will be and where will the emergency occurrences take place. These models consider the minimization of the maximum regret and the maximization of the minimum coverage. They are based on a previous work from the same authors, where they develop a model with innovative features like the possibility of vehicle substitution and the explicit consideration of vehicle unavailability by also representing the dispatching of the vehicles. The developed robust stochastic models have been applied to a dataset composed of Monte Carlo simulation scenarios that were generated from the analysis of real data patterns. Computational results are presented and discussed.

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Acknowledgments

This study has been funded by national funds, through FCT, Portuguese Science Foundation, under project UIDB/00308/2020 and with the collaboration of Coimbra Pediatric Hospital – Coimbra Hospital and University Center.

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Authors and Affiliations

  1. Universidade de Coimbra, CeBER, Faculdade de Economia, Coimbra, Portugal

    José Nelas & Joana Dias

  2. Hospital Pediátrico - CHUC, Coimbra, Portugal

    José Nelas

  3. Universidade de Coimbra, INESC-Coimbra, Coimbra, Portugal

    Joana Dias

Authors
  1. José Nelas
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  2. Joana Dias
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Correspondence to Joana Dias .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Nelas, J., Dias, J. (2020). Locating Emergency Vehicles: Robust Optimization Approaches. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12251. Springer, Cham. https://doi.org/10.1007/978-3-030-58808-3_41

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  • DOI: https://doi.org/10.1007/978-3-030-58808-3_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58807-6

  • Online ISBN: 978-3-030-58808-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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