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A Model-Driven Decision Support System for Aid in a Natural Disaster

  • Juan SepulvedaEmail author
  • Jessica Bull
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1026)

Abstract

This article deals with the architecture of a support system for helping decision makers in optimizing aid distribution during natural disaster situations. In such type of events, one of the critical tasks is the transportation of staff, people, food and medicines. Given the complexity of the operations scheduling, the article proposes a model-driven decision support system with an embedded module for solving a vehicle routing problem.

Keywords

Natural disasters logistics Decision support systems Routing 

Notes

Acknowledgments

The authors wish to thank the Industrial Engineering Department and DICYT of the U. of Santiago de Chile for their support. Special recognitions to former Col. Mr. Victor Drake of FACH for his cooperation during the scenario reconstruction.

References

  1. 1.
    Hallegatte, S., Przyluski, V.: The economics of natural disasters. CESifo Forum 11(2), 14–24 (2010)Google Scholar
  2. 2.
    Manopiniwes, W., Irohara, T.: A review of relief supply chain optimization. Ind. Eng. Manag. Syst. 13(1), 1–14 (2014)Google Scholar
  3. 3.
    Guha-Sapir, D., Hoyois Ph., Wallemacq P., Below. R.: Annual Disaster Statistical Review 2016: The Numbers and Trends. CRED, Brussels (2016)Google Scholar
  4. 4.
    Wan, M.: Public health emergencies. J. Pediatr. Child Health 2(3) (2003)Google Scholar
  5. 5.
    Endsley, M.: Towards a theory of situational awareness in dynamic systems. Hum. Factors 37(1), 32–64 (1995)CrossRefGoogle Scholar
  6. 6.
    Costa Freitas, M., Xavier, A., Fragoso, R.: An integrated decision support system for the Mediterranean forests. Land Use Policy 80, 298–308 (2019)CrossRefGoogle Scholar
  7. 7.
    Malmir, B., Amini, M., Chang, S.: A medical decision support system for disease diagnosis under uncertainty. Expert Syst. Appl. 88, 95–108 (2017)CrossRefGoogle Scholar
  8. 8.
    Filip, F., Constantin-Bala, Z., Ciurea, C.: Computer-Supported Collaborative Decision-Making, pp 31–69. Springer (2017)Google Scholar
  9. 9.
    Power, D.: Supporting Decision-Makers: An Expanded Framework. Informing Science - Challenges to Informing Clients, A Transdisciplinary Approach, June 2001. http://www.dssresources.com
  10. 10.
    Bula, G., Gonzalez, F.A., Prodhon, C., Afsar, H., Velasco, N.: Mixed integer linear programming model for vehicle routing problem for hazardous materials transportation. IFAC-PapersOnLine 49(12), 538–543 (2016)CrossRefGoogle Scholar
  11. 11.
    Tavakkoli-Moghaddam, R., Safaei, N., Kah, M., Rabbani, M.: A new capacitated vehicle routing problem with split service for minimizing fleet cost by simulated annealing. J. Franklin Inst. 344(5), 406–425 (2007)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Desrochers, M., Laporte, G.: Improvements and extensions to the Miller-Tucker-Zemlin subtour elimination constraints. Oper. Res. Lett. 10(1), 27–36 (1991)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Cattaruzza, D., Absi N., Feillet D.: Vehicle routing problems with multiple trips. 4OR 3 (2016)Google Scholar
  14. 14.
    Sepúlveda, J.M., Arriagada, I.A., Derpich, I.S.: A decision support system for distribution of supplies in natural disaster situations. In: IEEE XPlore Digital Library, Proceedings of 7th International Conference on Computers, Communications and Control, Oradea, Romania (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Universidad de Santiago de ChileSantiagoChile
  2. 2.Universidad AustralPuerto MonttChile

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