A clustering optimization approach for disaster relief delivery: A case study in Lima-Perú

Jorge Vargas-Florez, Rosario Medina-Rodríguez, Rafael Alva-Cabrera

Producción científica: Contribución a una revistaArtículo de la conferenciarevisión exhaustiva


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 twothirds from the total logistics cost. Therefore, a frequent problem in a disaster relief is to reduce the transportation cost by keeping an acceptable distribution service. The latter depends on a reliable delivery route design, which is not evident considering a post-disaster environment. In this case, the infrastructures and sources could be inexistent, unavailable or inoperative. This paper tackles this problem, regarding the constraints, to relief delivery in a post-disaster environment (like an eight degree earthquake) in the capital of Perú. The routes found by the hierarchical ascending clustering approach, which has been solved with a heuristic model, achieved the best result.

Idioma originalInglés
Páginas (desde-hasta)122-129
Número de páginas8
PublicaciónCEUR Workshop Proceedings
EstadoPublicada - 2016
Evento3rd Annual International Symposium on Information Management and Big Data, SIMBig 2016 - Cusco, Perú
Duración: 1 set. 20163 set. 2016


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