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Volume 2018, Article ID 1254794, 10 pages
Research Article

Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks

Engineering Department, Autonomous University of Hidalgo State, Carr. Pachuca-Tulancingo, Col. Carboneras, 42184 Mineral de la Reforma, HGO, Mexico

Correspondence should be addressed to Joselito Medina-Marin; xm.ude.heau@anidemj

Received 5 November 2017; Accepted 21 December 2017; Published 31 January 2018

Academic Editor: Julio Blanco-Fernández

Copyright © 2018 Federico Nuñez-Piña 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.


The problem of assigning buffers in a production line to obtain an optimum production rate is a combinatorial problem of type NP-Hard and it is known as Buffer Allocation Problem. It is of great importance for designers of production systems due to the costs involved in terms of space requirements. In this work, the relationship among the number of buffer slots, the number of work stations, and the production rate is studied. Response surface methodology and artificial neural network were used to develop predictive models to find optimal throughput values. 360 production rate values for different number of buffer slots and workstations were used to obtain a fourth-order mathematical model and four hidden layers’ artificial neural network. Both models have a good performance in predicting the throughput, although the artificial neural network model shows a better fit () against the response surface methodology (). Moreover, the artificial neural network produces better predictions for data not utilized in the models construction. Finally, this study can be used as a guide to forecast the maximum or near maximum throughput of production lines taking into account the buffer size and the number of machines in the line.