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Volume 2018, Article ID 3418580, 13 pages
Research Article

Optimizing a Biobjective Production-Distribution Planning Problem Using a GRASP

1Departamento de Actuaría, Física y Matemáticas, Universidad de las Américas Puebla, Santa Catarina Mártir s/n, 72810 San Andrés Cholula, PUE, Mexico
2Facultad de Ciencias Físico-Matemáticas, Universidad Autónoma de Nuevo León, Av. Universidad s/n, 66450 San Nicolás de los Garza, NL, Mexico
3Faculty of Engineering and Applied Sciences, Universidad de los Andes Chile, Monseñor Álvaro Portillo 12455, Las Condes, Santiago, Chile
4Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, 03920 Ciudad de México, Mexico
5Instituto Tecnológico de Sonora Unidad Navojoa, Ramón Corona S/N, Esq. con Aguascalientes, Col. ITSON, Navojoa, SON, Mexico

Correspondence should be addressed to José-Fernando Camacho-Vallejo; xm.ude.lnau@lvohcamac.esoj

Received 2 November 2017; Accepted 8 January 2018; Published 13 February 2018

Academic Editor: Jorge Luis García-Alcaraz

Copyright © 2018 Martha-Selene Casas-Ramírez 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 addresses a biobjective production-distribution planning problem. The problem is formulated as a mixed integer programming problem with two objectives. The objectives are to minimize the total costs and to balance the total workload of the supply chain, which consist of plants and depots, considering that it represents a company vertically integrated. In order to solve the model, we propose an adapted biobjective GRASP to obtain an approximation of the Pareto front. To evaluate the performance of the proposed algorithm, numerical experimentations are conducted over a set of instances used for similar problems. Results indicate that the proposed GRASP obtains a relatively small number of nondominated solutions for each tested instance in very short computational time. The approximated Pareto fronts are discontinuous and nonconvex. Moreover, the solutions clearly show the compromise between both objective functions.