Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks
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....
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/1254794 |
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| author | Federico Nuñez-Piña Joselito Medina-Marin Juan Carlos Seck-Tuoh-Mora Norberto Hernandez-Romero Eva Selene Hernandez-Gress |
| author_facet | Federico Nuñez-Piña Joselito Medina-Marin Juan Carlos Seck-Tuoh-Mora Norberto Hernandez-Romero Eva Selene Hernandez-Gress |
| author_sort | Federico Nuñez-Piña |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c1c488191f0545b49672933fbb7f5d3b |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-c1c488191f0545b49672933fbb7f5d3b2025-02-03T06:01:33ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/12547941254794Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural NetworksFederico Nuñez-Piña0Joselito Medina-Marin1Juan Carlos Seck-Tuoh-Mora2Norberto Hernandez-Romero3Eva Selene Hernandez-Gress4Engineering Department, Autonomous University of Hidalgo State, Carr. Pachuca-Tulancingo, Col. Carboneras, 42184 Mineral de la Reforma, HGO, MexicoEngineering Department, Autonomous University of Hidalgo State, Carr. Pachuca-Tulancingo, Col. Carboneras, 42184 Mineral de la Reforma, HGO, MexicoEngineering Department, Autonomous University of Hidalgo State, Carr. Pachuca-Tulancingo, Col. Carboneras, 42184 Mineral de la Reforma, HGO, MexicoEngineering Department, Autonomous University of Hidalgo State, Carr. Pachuca-Tulancingo, Col. Carboneras, 42184 Mineral de la Reforma, HGO, MexicoEngineering Department, Autonomous University of Hidalgo State, Carr. Pachuca-Tulancingo, Col. Carboneras, 42184 Mineral de la Reforma, HGO, MexicoThe 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.http://dx.doi.org/10.1155/2018/1254794 |
| spellingShingle | Federico Nuñez-Piña Joselito Medina-Marin Juan Carlos Seck-Tuoh-Mora Norberto Hernandez-Romero Eva Selene Hernandez-Gress Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks Complexity |
| title | Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks |
| title_full | Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks |
| title_fullStr | Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks |
| title_full_unstemmed | Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks |
| title_short | Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks |
| title_sort | modeling of throughput in production lines using response surface methodology and artificial neural networks |
| url | http://dx.doi.org/10.1155/2018/1254794 |
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