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A Mathematical Pre-Disaster Model with Uncertainty and Multiple Criteria for Facility Location and Network Fortification

Author

Listed:
  • Julia Monzón

    (Department of Statistics and Operational Research, Complutense University of Madrid, 28040 Madrid, Spain)

  • Federico Liberatore

    (School of Computer Science & Informatics, Cardiff University, Cardiff CF24 3AA, UK
    UC3M-Santander Big Data Institute (IBiDat), Charles III University of Madrid, 28903 Getafe, Spain)

  • Begoña Vitoriano

    (Department of Statistics and Operational Research and Institute of Interdisciplinary Mathematics, Complutense University of Madrid, 28040 Madrid, Spain)

Abstract

Disasters have catastrophic effects on the affected population, especially in developing and underdeveloped countries. Humanitarian Logistics models can help decision-makers to efficiently and effectively warehouse and distribute emergency goods to the affected population, to reduce casualties and suffering. However, poor planning and structural damage to the transportation infrastructure could hamper these efforts and, eventually, make it impossible to reach all the affected demand centers. In this paper, a pre-disaster Humanitarian Logistics model is presented that jointly optimizes the prepositioning of aid distribution centers and the strengthening of road sections to ensure that as much affected population as possible can efficiently get help. The model is stochastic in nature and considers that the demand in the centers affected by the disaster and the state of the transportation network are random. Uncertainty is represented through scenarios representing possible disasters. The methodology is applied to a real-world case study based on the 2018 storm system that hit the Nampula Province in Mozambique.

Suggested Citation

  • Julia Monzón & Federico Liberatore & Begoña Vitoriano, 2020. "A Mathematical Pre-Disaster Model with Uncertainty and Multiple Criteria for Facility Location and Network Fortification," Mathematics, MDPI, vol. 8(4), pages 1-17, April.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:4:p:529-:d:341031
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    References listed on IDEAS

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