Abstract
The focus of this work is to enable the dignified survival of the people affected in a natural disaster through the optimal location of pre-positioned warehouses and the determination of their optimal inventory levels aiming to provide emergency supplies efficiently. To this end, a mixed-integer nonlinear mathematical model is presented to be applied during the disaster preparation phase. The mathematical model integrates two elements immersed in humanitarian logistics; one that is based on determining the optimal locations for the installation of pre-positioned warehouses, and the other that approaches the establishment of the inventory levels in these facilities to supply food, equipment, water, and vaccines to different kinds of groups of survivors classified according to the human life cycle. The mathematical model is validated through the analysis of two regions of the State of Veracruz, the Mexican state with the highest number of declarations of emergency related to hydrometeorological phenomena. The installation of instances with two and three pre-positioned warehouses, each with a total of 29 municipalities, 14 of which have been subject to the issuance of declarations of emergency, are presented. The results show the optimal location for the pre-positioned warehouses and the number of kits allocated to each one depending on the type of demand. The model is a decision support instrument designed for the use of planners for the timely and efficient response to disasters to aid the people affected by a natural phenomenon, especially one of the hydrometeorological type.
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Notes
- 1.
The WHO mentions that pregnancy and puerperium are particular stages of life in which there are particular nutritional needs, these stages will not be considered in the model (WHO, 2017).
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Barojas-Payán, E., Sánchez-Partida, D., Gibaja-Romero, D.E., Martínez-Flores, J.L., Cabrera-Rios, M. (2021). Optimization Model to Locate Pre-positioned Warehouses and Establish Humanitarian Aid Inventory Levels. In: Disaster Risk Reduction in Mexico. Springer, Cham. https://doi.org/10.1007/978-3-030-67295-9_8
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