Abstract
Value streams are attributed graphs used for modeling and simulating production processes. We suggest a machine learning-based approach to identify and repair modeling errors in value streams, specifically incorrect edges or product annotations. Our approach recasts graph attribution as a link prediction problem and uses graph-based features describing the local constellation in the value stream, such as the classes of successor and predecessor nodes or the product consistency in the material flow. By wrapping our model – which suggests single repair steps – into a beam search process, we can derive entire repair sequences. An expert study shows that for all 16 constellations tested, our model suggests the right changes to repair typical errors. Furthermore, our experiments based on five simultaneous random edge corruptions on a set of 70 value streams achieves an average precision up to 96.4%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
With regular nodes we refer to all non-product nodes.
- 4.
Feature lengths are given in parenthesis.
References
Becker, T., Funke, T.: Machine learning methods for prediction of changes in material flow networks. Procedia CIRP 93, 485–490 (2020). https://doi.org/10.1016/j.procir.2020.04.030
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011). https://doi.org/10.5555/1953048.2078195
Rossi, A., Barbosa, D., Firmani, D., Matinata, A., Merialdo, P.: Knowledge graph embedding for link prediction: a comparative analysis. ACM Trans. Knowl. Discov. Data 15(2), 14:1–14:49 (2021). https://doi.org/10.1145/3424672
Rother, M., Shook, J.: Learning to See: Value Stream Mapping to Create Value and Eliminate Muda. Lean Enterprise Institute (1999)
Schönemann, M., Kurle, D., Herrmann, C., Thiede, S.: Multi-product EVSM simulation. Procedia CIRP 41, 334–339 (2016)
Sun, Y., Han, J.: Mining heterogeneous information networks: a structural analysis approach. SIGKDD Explor. 14(2), 20–28 (2012). https://doi.org/10.1145/2481244.2481248
Uriarte, A.G., Ng, A.H.C., Moris, M.U.: Bringing together lean and simulation: a comprehensive review. Int. J. Prod. Res. 58(1), 87–117 (2020). https://doi.org/10.1080/00207543.2019.1643512
Vernickel, K., et al.: Machine-learning-based approach for parameterizing material flow simulation models. Procedia CIRP 93, 407–412 (2020). https://doi.org/10.1016/j.procir.2020.04.018
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks (2021). https://doi.org/10.1109/TNNLS.2020.2978386
Acknowledgements
This work was supported by the Federal State of Hesse (Research Program “Distral”, project “AI-Mod”, project ID 493 20_0051_2A).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wrzalik, M. et al. (2023). Value Stream Repair Using Graph Structure Learning. In: Fujita, H., Wang, Y., Xiao, Y., Moonis, A. (eds) Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2023. Lecture Notes in Computer Science(), vol 13926. Springer, Cham. https://doi.org/10.1007/978-3-031-36822-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-36822-6_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36821-9
Online ISBN: 978-3-031-36822-6
eBook Packages: Computer ScienceComputer Science (R0)