Publicado

2020-01-01

Aluminum bar cutting optimization for door and window manufacturing

Optimización de corte de barras de aluminio para la fabricación de puertas y ventanas

DOI:

https://doi.org/10.15446/dyna.v87n212.82636

Palabras clave:

one-dimensional cut, mathematical modeling, aluminum (en)
corte unidimensional, modelo matemático, aluminio (es)

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Autores/as

This study aims to optimize the one-dimensional cutting process of aluminum bars for the production of aluminum doors. Reducing the use of bars and the amount of material that becomes scrap is a key factor in process efficiency, reducing the need for raw material procurement. The mathematical model used considers the size of the bar, the number and size of cuts, the size of the leftovers that can be used and the size of the leftovers that are considered scrap. Based on real data from a company in the aluminum frame segment, the mathematical model was used to simulate three different scenarios. Three different objective functions were used in the simulations, and the results obtained in each scenario were described in order to indicate the advantages and disadvantages of using each objective function. For the instance sizes studied, the model is able to obtain optimal solutions with little computational time.

El presente trabajo tiene como objetivo optimizar el proceso de corte unidimensional de barras de aluminio para la producción de puertas de aluminio. Reducir el uso de barras y la cantidad de material que se convierte en chatarra es un factor clave en la eficiencia del proceso, reduciendo la necesidad de adquisición de materia prima. El modelo matemático utilizado considera el tamaño de la barra, el número y el tamaño de los cortes, el tamaño sobrante a que se puede usar y el tamaño sobrante que se considera chatarra. Basado en datos reales de una compañía en el segmento de marcos de aluminio, el modelo matemático se utilizó para simular tres escenarios diferentes. Se utilizaron tres funciones objetivo diferentes para el mismo modelo en las simulaciones y se describieron los resultados obtenidos en cada escenario para las tres funciones con el fin de indicar las ventajas y desventajas de usar cada función objetivo. Para los tamaños de instancia estudiados, el modelo puede obtener soluciones óptimas con poco tiempo computacional.

Referencias

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Cómo citar

IEEE

[1]
A. A. Machado, J. C. Zayatz, M. M. da Silva, G. Melluzzi Neto, G. C. L. Leal, y R. H. Palma Lima, «Aluminum bar cutting optimization for door and window manufacturing», DYNA, vol. 87, n.º 212, pp. 155–162, ene. 2020.

ACM

[1]
Machado, A.A., Zayatz, J.C., da Silva, M.M., Melluzzi Neto, G., Leal, G.C.L. y Palma Lima, R.H. 2020. Aluminum bar cutting optimization for door and window manufacturing. DYNA. 87, 212 (ene. 2020), 155–162. DOI:https://doi.org/10.15446/dyna.v87n212.82636.

ACS

(1)
Machado, A. A.; Zayatz, J. C.; da Silva, M. M.; Melluzzi Neto, G.; Leal, G. C. L.; Palma Lima, R. H. Aluminum bar cutting optimization for door and window manufacturing. DYNA 2020, 87, 155-162.

APA

Machado, A. A., Zayatz, J. C., da Silva, M. M., Melluzzi Neto, G., Leal, G. C. L. & Palma Lima, R. H. (2020). Aluminum bar cutting optimization for door and window manufacturing. DYNA, 87(212), 155–162. https://doi.org/10.15446/dyna.v87n212.82636

ABNT

MACHADO, A. A.; ZAYATZ, J. C.; DA SILVA, M. M.; MELLUZZI NETO, G.; LEAL, G. C. L.; PALMA LIMA, R. H. Aluminum bar cutting optimization for door and window manufacturing. DYNA, [S. l.], v. 87, n. 212, p. 155–162, 2020. DOI: 10.15446/dyna.v87n212.82636. Disponível em: https://revistas.unal.edu.co/index.php/dyna/article/view/82636. Acesso em: 8 mar. 2026.

Chicago

Machado, Ageu Araujo, João Carlos Zayatz, Marcos Meurer da Silva, Guilherme Melluzzi Neto, Gislaine Camila Lapasini Leal, y Rafael Henrique Palma Lima. 2020. «Aluminum bar cutting optimization for door and window manufacturing». DYNA 87 (212):155-62. https://doi.org/10.15446/dyna.v87n212.82636.

Harvard

Machado, A. A., Zayatz, J. C., da Silva, M. M., Melluzzi Neto, G., Leal, G. C. L. y Palma Lima, R. H. (2020) «Aluminum bar cutting optimization for door and window manufacturing», DYNA, 87(212), pp. 155–162. doi: 10.15446/dyna.v87n212.82636.

MLA

Machado, A. A., J. C. Zayatz, M. M. da Silva, G. Melluzzi Neto, G. C. L. Leal, y R. H. Palma Lima. «Aluminum bar cutting optimization for door and window manufacturing». DYNA, vol. 87, n.º 212, enero de 2020, pp. 155-62, doi:10.15446/dyna.v87n212.82636.

Turabian

Machado, Ageu Araujo, João Carlos Zayatz, Marcos Meurer da Silva, Guilherme Melluzzi Neto, Gislaine Camila Lapasini Leal, y Rafael Henrique Palma Lima. «Aluminum bar cutting optimization for door and window manufacturing». DYNA 87, no. 212 (enero 1, 2020): 155–162. Accedido marzo 8, 2026. https://revistas.unal.edu.co/index.php/dyna/article/view/82636.

Vancouver

1.
Machado AA, Zayatz JC, da Silva MM, Melluzzi Neto G, Leal GCL, Palma Lima RH. Aluminum bar cutting optimization for door and window manufacturing. DYNA [Internet]. 1 de enero de 2020 [citado 8 de marzo de 2026];87(212):155-62. Disponible en: https://revistas.unal.edu.co/index.php/dyna/article/view/82636

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CrossRef Cited-by

CrossRef citations6

1. Irving Barragan-Vite, Joselito Medina-Marin, Norberto Hernandez-Romero, Gustavo Erick Anaya-Fuentes. (2024). A Petri Net-Based Algorithm for Solving the One-Dimensional Cutting Stock Problem. Applied Sciences, 14(18), p.8172. https://doi.org/10.3390/app14188172.

2. Leonardo Javier Montiel-Arrieta, Irving Barragan-Vite, Juan Carlos Seck-Tuoh-Mora, Norberto Hernandez-Romero, Manuel González-Hernández, Joselito Medina-Marin. (2023). Minimizing the total waste in the one-dimensional cutting stock problem with the African buffalo optimization algorithm. PeerJ Computer Science, 9, p.e1728. https://doi.org/10.7717/peerj-cs.1728.

3. Victor Senergues, Nadjib Brahimi, Adriana Cristina Cherri, François Klein, Olivier Péton. (2026). Cutting stock problem with usable leftovers: A review. European Journal of Operational Research, 328(1), p.1. https://doi.org/10.1016/j.ejor.2025.03.014.

4. Özge KÖKSAL, Ergün EROĞLU. (2023). Solution Approach to Cutting Stock Problems Using Iterative Trim Loss Algorithm and Monte-Carlo Simulation. Alphanumeric Journal, 11(2), p.125. https://doi.org/10.17093/alphanumeric.1293487.

5. Rowan Abdelhafez, Mostafa Touny. (2024). A Comprehensive Inventory Management System for UPVC Companies: Optimizing Cutting, Simplifying Ordering. 2024 Intelligent Methods, Systems, and Applications (IMSA). , p.647. https://doi.org/10.1109/IMSA61967.2024.10652797.

6. Tzu-Jan Tung, Mohamed Al-Hussein, Pablo Martinez. (2023). Vision-Based Guiding System for Autonomous Robotic Corner Cleaning of Window Frames. Buildings, 13(12), p.2990. https://doi.org/10.3390/buildings13122990.

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