Design of a Logistics Nonlinear System for a Complex, Multiechelon, Supply Chain Network with Uncertain Demands

Aaron Guerrero Campanur; Olivares-Benitez, Elias; Pablo A. Miranda; Rodolfo Eleazar Perez-Loaiza; Jose Humberto Ablanedo-Rosas

doi:10.1155/2018/4139601

Design of a Logistics Nonlinear System for a Complex, Multiechelon, Supply Chain Network with Uncertain Demands

Journal

Complexity

ISSN

1076-2787

1099-0526

Date Issued

2018

Author(s)

Aaron Guerrero Campanur

Olivares-Benitez, Elias

Facultad de Ingeniería - CampGDL

Pablo A. Miranda

Rodolfo Eleazar Perez-Loaiza

Jose Humberto Ablanedo-Rosas

Type

text::journal::journal article

DOI

10.1155/2018/4139601

URL

https://scripta.up.edu.mx/handle/20.500.12552/2859

Abstract

Industrial systems, such as logistics and supply chain networks, are complex systems because they comprise a big number of interconnected actors and significant nonlinear and stochastic features. This paper analyzes a distribution network design problem for a four-echelon supply chain. The problem is represented as an inventory-location model with uncertain demand and a continuous review inventory policy. The decision variables include location at the intermediate levels and product flows between echelons. The related safety and cyclic inventory levels can be computed from these decision variables. The problem is formulated as a mixed integer nonlinear programming model to find the optimal design of the distribution network. A linearization of the nonlinear model based on a piecewise linear approximation is proposed. The objective function and nonlinear constraints are reformulated as linear formulations, transforming the original nonlinear problem into a mixed integer linear programming model. The proposed approach was tested in 50 instances to compare the nonlinear and linear formulations. The results prove that the proposed linearization outperforms the nonlinear formulation achieving convergence to a better local optimum with shorter computational time. This method provides flexibility to the decision-maker allowing the analysis of scenarios in a shorter time.