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Journal of Optimization
Volume 2018, Article ID 3605298, 14 pages
Research Article

Extended GRASP-Capacitated -Means Clustering Algorithm to Establish Humanitarian Support Centers in Large Regions at Risk in Mexico

Universidad Popular Autonoma del Estado de Puebla, A.C., Postgraduate Department of Logistics and Supply Chain Management, Puebla, Mexico

Correspondence should be addressed to Santiago-Omar Caballero-Morales; xm.peapu@orellabac.ramoogaitnas

Received 9 July 2018; Revised 25 November 2018; Accepted 4 December 2018; Published 20 December 2018

Guest Editor: Morteza Ghobakhloo

Copyright © 2018 Santiago-Omar Caballero-Morales et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Mexico is located within the so-called Fire Belt which makes it susceptible to earthquakes. In fact, two-thirds of the Mexican territory have a significant seismic risk. On the other hand, the country’s location in the tropical zone makes it susceptible to hurricanes which are generated in both the Pacific and Atlantic Oceans. Due to these situations, each year many communities are affected by diverse natural disasters in Mexico and efficient logistic systems are required to provide prompt support. This work is aimed at providing an efficient metaheuristic to determine the most appropriate location for support centers in the State of Veracruz, which is one of the most affected regions in Mexico. The metaheuristic is based on the -Means Clustering (KMC) algorithm which is extended to integrate (a) the associated capacity restrictions of the support centers, (b) a micro Genetic Algorithm GA to estimate a search interval for the most suitable number of support centers, (c) variable number of assigned elements to centers in order to add flexibility to the assignation task, and (d) random-based decision model to further improve the final assignments. These extensions on the KMC algorithm led to the GRASP-Capacitated -Means Clustering (GRASP-CKMC) algorithm which was able to provide very suitable solutions for the establishment of 260 support centers for 3837 communities at risk in Veracruz, Mexico. Validation of the GRASP-CKMC algorithm was performed with well-known test instances and metaheuristics. The validation supported its suitability as alternative to standard metaheuristics such as Capacitated -Means (CKM), Genetic Algorithms (GA), and Variable Neighborhood Search (VNS).