Abstract
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.
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Acknowledgement
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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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. https://doi.org/10.1007/978-3-319-55209-5_6
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DOI: https://doi.org/10.1007/978-3-319-55209-5_6
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