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Advances in Operations Research
Volume 2017, Article ID 4093689, 11 pages
Research Article

Insular Biobjective Routing with Environmental Considerations for a Solid Waste Collection System in Southern Chile

1Universidad Popular Autónoma del Estado de Puebla, 21 sur 1103 Barrio de Santiago, 72410 Puebla, PUE, Mexico
2Universidad Panamericana, Prolongación Calzada Circunvalación Poniente 49, 45010 Zapopan, JAL, Mexico
3Pontificia Universidad Católica de Valparaíso, Av. Brasil No. 2950, Casilla 4059, Valparaíso, Chile

Correspondence should be addressed to Elias Olivares-Benitez; xm.ude.pu@bseraviloe

Received 6 March 2017; Accepted 22 June 2017; Published 10 August 2017

Academic Editor: Juan C. Leyva

Copyright © 2017 Daniela S. Arango González 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.


This paper presents a biobjective problem for a solid waste collection system in a set of islands in southern Chile. The first objective minimizes transportation cost and the second one reduces the environmental impact caused by the accumulation of solid waste at the collection points. To solve this problem, biobjective mixed integer linear programming is used. In the model, an itinerary scheme is considered for the visit to the islands. The model decides which collection points are visited per island, the collection pattern, and quantity of solid waste to be collected at each site. The quantity of solid waste is obtained dividing the solid waste generated in the island by the number of collection points selected in that same island and the frequency of visits. For this problem, we considered that the environmental impact function varies through the days during which solid waste is accumulated at each collection point. We present an instance based on real data for a set of islands in Chiloe and Palena regions in southern Chile, in which the deposit node is Dalcahue. We used the epsilon-constraint method and the weighted sum method to obtain the Pareto front, using commercial optimization software.