A three echelon, multiple-source, capacitated facility location problem for solid waste management at the northern region of Veracruz, Mexico

Mexico is the world's third consumer of Polyethylene Terephthalate (PET) with 225 bottles used per capita per year. It can be argued that the main problem regarding pollution generated by PET waste lies on the lack of an appropriate solid waste management. The decision regarding facility locati...

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Bibliographic Details
Published inIISE Annual Conference. Proceedings p. 2049
Main Authors Pérez-Salazar, María del Rosario, Mateo-Díaz, Nicolás Francisco, Rodríguez, Rogelio García, Cristóbal-Vázquez, Isidro Marco Antonio, Aguilar-Lasserre, Alberto A
Format Journal Article
LanguageEnglish
Published Norcross Institute of Industrial and Systems Engineers (IISE) 01.01.2014
Subjects
Commodities
Customers
Genetic algorithms
Landfill
Linear programming
Logistics
Polyethylene terephthalate
Raw materials
Towns
Waste disposal
Waste management
Mexico
Veracruz Mexico
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Summary:Mexico is the world's third consumer of Polyethylene Terephthalate (PET) with 225 bottles used per capita per year. It can be argued that the main problem regarding pollution generated by PET waste lies on the lack of an appropriate solid waste management. The decision regarding facility location is the central issue in solid waste management. This work focuses in the development of a two echelon, multi-source, capacitated facility location problem described as follows: given the quantities of PET generated at the northern region of Veracruz, Mexico, by considering five cities and each as a single generation source, a collection center has to be selected among a set o f pre-identified locations in the town of Tempoal, Veracruz; in order to serve a set of demands points in the re-use market so that the overall cost of the system is minimized. A mixed integer linear programming model is proposed, additionally in order to gain a more realistic-based decision, demands are assumed to be uncertain, and a genetic algorithm is designed for the optimization model.