Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks
Joint Authors
Nuñez-Piña, Federico
Medina-Marin, Joselito
Seck-Tuoh-Mora, Juan Carlos
Hernandez-Romero, Norberto
Hernandez-Gress, Eva Selene
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-01-31
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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 (R=1.0000) against the response surface methodology (R=0.9996).
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.
American Psychological Association (APA)
Nuñez-Piña, Federico& Medina-Marin, Joselito& Seck-Tuoh-Mora, Juan Carlos& Hernandez-Romero, Norberto& Hernandez-Gress, Eva Selene. 2018. Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks. Complexity،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1132700
Modern Language Association (MLA)
Nuñez-Piña, Federico…[et al.]. Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks. Complexity No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1132700
American Medical Association (AMA)
Nuñez-Piña, Federico& Medina-Marin, Joselito& Seck-Tuoh-Mora, Juan Carlos& Hernandez-Romero, Norberto& Hernandez-Gress, Eva Selene. Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks. Complexity. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1132700
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1132700