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A Clustering Optimization Approach for Disaster Relief Delivery: A Case Study in Lima-Perú

Part of the Communications in Computer and Information Science book series (CCIS,volume 656)


During the last decade, funds to face humanitarian operations have increased approximately ten times. According to the Global Humanitarian Assistance Report, in 2013 the humanitarian funding requirement was by US$ 22 billion, which represents \(27.2\%\) more than the requested in 2012. Furthermore, the transportation cost represents between one-third to two-thirds from the total logistics cost. Therefore, a frequent problem in a disaster relief is to reduce the transportation cost by keeping an adequate distribution service. The latter depends on a reliable delivery route design, which is not easy to do considering a post-disaster environment, where the infrastructures and sources could be inexistent, unavailable or inoperative. This paper tackles this problem, regarding the constraints, to deliver relief aids in a post-disaster state (like an eight-degree earthquake) in the capital of Perú. The routes found by the hierarchical ascending clustering approach, solved with a heuristic model, achieved a sufficient and satisfactory solution.


  • Vehicle Route Problem
  • Transportation Time
  • Vehicle Route
  • Real Distance
  • Travel Salesperson Problem

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  1. Assessment, U.N.D., Coordination: Field handbook: UNDAC (2006)

    Google Scholar 

  2. Beamon, B.M., Balcik, B.: Performance measurement in humanitarian relief chains. Int. J. Public Sect. Manag. 21(1), 4–25 (2008)

    CrossRef  Google Scholar 

  3. Clarke, G., Wright, J.W.: Scheduling of vehicles from a central depot to a number of delivery points. Oper. Res. 12(4), 568–581 (1964)

    CrossRef  Google Scholar 

  4. Gong, Q., Batta, R.: Allocation and reallocation of ambulances to casualty clusters in a disaster relief operation. IIE Trans. 39(1), 27–39 (2007)

    CrossRef  Google Scholar 

  5. Hall, M.: Supply chain management in the humanitarian context: anatomy of effective relief and development chains. Master thesis, Webster University (2012)

    Google Scholar 

  6. Justel, A.: Técnicas de análisis multivariante para agrupación: Métodos cluster (2008)

    Google Scholar 

  7. Klibi, W., Martel, A.: Modeling approaches for the design of resilient supply networks under disruptions. Int. J. Prod. Econ. 135(2), 882–898 (2012)

    CrossRef  Google Scholar 

  8. Leseure, M., Hudson-Smith, M., Chandes, J., Paché, G.: Investigating humanitarian logistics issues: from operations management to strategic action. J. Manuf. Technol. Manag. 21(3), 320–340 (2010)

    CrossRef  Google Scholar 

  9. Martinez, A.J.P., Stapleton, O., Van Wassenhove, L.N.: Field vehicle fleet management in humanitarian operations: a case-based approach (2011)

    Google Scholar 

  10. Matthew, A., Elliott, R.J., Toshihiro, O., Strobl, E.: Natural disasters, industrial clusters and manufacturing plant survival (2015)

    Google Scholar 

  11. Özdamar, L., Demir, O.: A hierarchical clustering and routing procedure for large scale disaster relief logistics planning. Transp. Res. Part E Logist. Transp. Rev. 48(3), 591–602 (2012)

    CrossRef  Google Scholar 

  12. Pidd, M., De Silva, F., Eglese, R.: A simulation model for emergency evacuation. Eur. J. Oper. Res. 90(3), 413–419 (1996)

    CrossRef  MATH  Google Scholar 

  13. Prins, C., Lacomme, P., Prodhon, C.: Order-first split-second methods for vehicle routing problems: a review. Transp. Res. Part C Emerg. Technol. 40, 179–200 (2014)

    CrossRef  Google Scholar 

  14. Serpa Oshiro, V.R.: Optimización y localización de almacenes de abastecimiento para la atención de un terremoto de gran magnitud en lima metropolitana y callao (2014)

    Google Scholar 

  15. Tai, C.A., Lee, Y.L., Lin, C.Y.: Urban disaster prevention shelter location and evacuation behavior analysis. J. Asian Archit. Build. Eng. 9(1), 215–220 (2010)

    CrossRef  Google Scholar 

  16. Thevenaz, C., Resodihardjo, S.L.: All the best laid plans\(\ldots \) conditions impeding proper emergency response. Int. J. Prod. Econ. 126(1), 7–21 (2010)

    CrossRef  Google Scholar 

  17. Toth, P., Vigo, D.: The Vehicle Routing Problem. Monographs on Discrete Mathematics and Applications. Society for Industrial and Applied Mathematics, Philadelphia (2002)

    CrossRef  MATH  Google Scholar 

  18. Villardón, J.L.V.: Introducción al análisis de clúster. Departamento de Estadística, Universidad de Salamanca. p. 22 (2007)

    Google Scholar 

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The authors are grateful to the Research Group on Crisis and Disaster Management (CID) of the Pontificia Universidad Católica del Perú, for their valuable support and collaboration to this research work.

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Correspondence to Rosario Medina-Rodríguez .

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Vargas-Florez, J., Medina-Rodríguez, R., Alva-Cabrera, R. (2017). A Clustering Optimization Approach for Disaster Relief Delivery: A Case Study in Lima-Perú. In: Lossio-Ventura, J., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig SIMBig 2015 2016. Communications in Computer and Information Science, vol 656. Springer, Cham.

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