Publicado

2017-01-01

Modelo biobjetivo para el problema de localización de centros de auxilio y distribución de productos en situaciones de respuesta a desastres

A bi-objective model for the location of relief centers and distribution of commodities in disaster response operations

Palabras clave:

optimización biobjetivo, logística humanitaria, localización-distribución, dimensionamiento de flota, programación estocástica (es)
bi-objective optimization, humanitarian logistic, location-distribution, fleet sizing, stochastic programming (en)

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Las consecuencias de los desastres naturales en los últimos años han evidenciado la complejidad de esos eventos. Entre las decisiones importantes en situaciones de desastres están la localización de centros de auxilio, la distribución de productos y el dimensionamiento de flota. La ejecución de estas operaciones puede implicar objetivos contradictorios, principalmente, costos logísticos y costos por demanda insatisfecha. Por un lado, minimizar la demanda insatisfecha lleva a mayores costos logísticos. Por otro lado, minimizar los costos logísticos sin considerar la demanda insatisfecha implica un atendimiento ineficaz en las áreas afectadas. En este artículo es desarrollado un modelo biobjetivo de programación estocástica para el problema integrado de localización-distribución y dimensionamiento de flota. Se solucionan instancias basadas en el megadesastre de la región Serrana de Rio de Janeiro en 2011. Las soluciones del modelo biobjetivo son comparadas con una versión mono-objetivo del mismo, resaltando las ventajas y desventajas de cada modelo.
Consequences of natural disasters in recent years have shown the complexity of these situations. In disaster situations, the location of relief centers, the distribution of supplies and the fleet sizing are some of the most important decisions. In most cases the execution of this operations lead to contradictory objectives, mainly, logistic costs and unmet demand costs. On the one hand, minimize unmet demand implies higher logistics costs. On the other hand, minimize logistics costs without considering the unmet demand may lead with an inefficient attendance in the affected areas. In this paper, we propose a bi-objective stochastic programing model for the integrated problem of location-distribution and fleet sizing. We solve instances based on the mega disaster in the Mountain Region of Rio de Janeiro in 2011. We compare the solutions of a bi-objective model in respect to solutions of a mono-objective version, highlighting the advantages and disadvantages of each model.

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Cómo citar

[1]
“Modelo biobjetivo para el problema de localización de centros de auxilio y distribución de productos en situaciones de respuesta a desastres”, DYNA, vol. 84, no. 200, pp. 356–366, Jan. 2017, doi: 10.15446/dyna.v84n200.54810.

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