Artificial Neural Network for Assembly Line Balancing
PDF

Keywords

Assembly, Line balancing, Artificial Neural Network.

How to Cite

Ucheagwu, P., Ugochukwu Okeke, J., I. Okonta, C., & Osamuyimwen, E. (2019). Artificial Neural Network for Assembly Line Balancing. American International Journal of Sciences and Engineering Research, 2(2), 69-78. https://doi.org/10.46545/aijser.v2i2.121

Abstract

This study examines an assembly line balancing using artificial neural network. An organization that balances the unique workloads must respect the limits and restrictions that hinder the assembly. To optimize the very specific operations, balancing an assembly line may require different methods, including: genetic algorithm, heuristic approach, simulation techniques, the ant colony optimization (ACO), etc., but in this study, artificial neural networks was applied to solving problems of assembly line balancing.  This study also explores the characteristics of the assembly line and the classification of the assembly balancing problems, suggesting as an artificial neural network solve.

https://doi.org/10.46545/aijser.v2i2.121
PDF

References

Ahmadi, R. H., & Wurgaft, H. (1994). Design for Manufacturing synchronized flow. Manage. Sci, 40, 1469-1483. DOI: 10.1287 / mnsc.40.11.1469

Baybars, I. (2016). A study of exact algorithms for the simple problem of assembly line balancing. Manage. Sci, 32, 909-932. DOI: 10.1287 / mnsc.32.8.909

Becker, C. & Scholl, A. (2006). A survey of problems and methods of balancing of diffuse assembly line. EUR. J. operate. Res, 168, 694-715 . DOI: 10.1016 / j.ejor.2004.07.023

Groover, M. P. (2008). Automation, Production Systems, and Computer Integrated Manufacturing. . 3rd Edition, Prentice Hall International, Inc., Upper Saddle River, New Jersey, Pp. 375.

Kim, Y. K., Kim, Y. J.,& Kim, Y. H.(1996). Genetic algorithms for the assembly of balance for many purposes. Comput. Ind Eng, 30, 397- 409. DOI: 10.1016 / 0360-8352 (96) 00009 5Kimms

Miltenburg, J.(2012). Balancing and sequencing production model lines as mixed.UTJ Flex. Manuf. Syst, 14, 119-151. DOI: 10.1023 / A: 1014434117888

Pitts, W. H., & McCulloch, W. S.(1947). As we know universals: the perception of auditory and visual forms. Bull. Mates. Biophys, 9, 127-147. DOI: 10.1007 / BF02478291

Priced, S. O., & Tunali, A.(2008). A review of the genetic algorithm in the financial statements of the assembly line. TJ Manuf, 19 :. 49-60. DOI: 10.1007 / s10845-007-0045-5

Tempelmeier, H. (2003). Practical considerations to optimize the production flow systems. TJ Prod Res., 41:. 149-170. DOI: 10.1080 / 00207540210161641
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Downloads

Download data is not yet available.