An intelligent decision support system for production planning based on machine learning
- 83 Downloads
This paper presents a new methodology to solve a Closed-Loop Supply Chain (CLSC) management problem through a decision-making system based on fuzzy logic built on machine learning. The system will provide decisions to operate a production plant integrated in a CLSC to meet the production goals with the presence of uncertainties. One of the main contributions of the proposal is the ability to reject the effects that the imbalances in the rest of the chain have on the inventories of raw materials and finished products. For this, an intelligent algorithm will be in charge of the supervision of the plant operation and task-reprogramming to ensure the achievement of the process goals. Fuzzy logic and machine learning techniques are combined to design the tool. The method was tested on an industrial hospital laundry with satisfactory results, thus highlighting the potential of this proposal for its incorporation into the Industry 4.0 framework.
KeywordsArtificial intelligence Intelligent manufacturing Machine learning Operation management Decision support system
José Manuel Gonzalez-Cava’s research was supported by the Spanish Ministry of Science, Innovation and Universities (http://www.ciencia.gob.es/) under the “Formación de Profesorado Universitario” Grant FPU15/03347.
Compliance with ethical standards
Conflict of interests
The authors declare that they have no conflict of interest.
- Aengchuan, P., & Phruksaphanrat, B. (2018). Comparison of fuzzy inference system (FIS), FIS with artificial neural networks (FIS + ANN) and FIS with adaptive neuro-fuzzy inference system (FIS + ANFIS) for inventory control. Journal of Intelligent Manufacturing,29(4), 905–923. https://doi.org/10.1007/s10845-015-1146-1.CrossRefGoogle Scholar
- Babapour Mofrad, R., Schoonenboom, N. S. M., Tijms, B. M., Scheltens, P., Visser, P. J., van der Flier, W. M., et al. (2019). Decision tree supports the interpretation of CSF biomarkers in Alzheimer’s disease. Alzheimer’s and Dementia: Diagnosis, Assessment and Disease Monitoring,11, 1–9. https://doi.org/10.1016/j.dadm.2018.10.004.CrossRefGoogle Scholar
- Bai, Y., Sun, Z., Zeng, B., Long, J., Li, L., de Oliveira, J. V., et al. (2019). A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction. Journal of Intelligent Manufacturing,30(5), 2245–2256. https://doi.org/10.1007/s10845-017-1388-1.CrossRefGoogle Scholar
- Bricogne, M., Le Duigou, J., & Eynard, B. (2016). Design processes of mechatronic systems. In P. Hehenberger & D. Bradley (Eds.), Mechatronic futures: Challenges and solutions for mechatronic systems and their designers (pp. 75–89). Cham: Springer. https://doi.org/10.1007/978-3-319-32156-1_6.CrossRefGoogle Scholar
- Coenen, J., van der Heijden, R. E. C. M., & van Riel, A. C. R. (2018). Understanding approaches to complexity and uncertainty in closed-loop supply chain management: Past findings and future directions. Journal of Cleaner Production,201, 1–13. https://doi.org/10.1016/j.jclepro.2018.07.216.CrossRefGoogle Scholar
- De’Ath, G., & Fabricius, K. E. (2000). Classification and regression trees: A powerful yet simple technique for ecological data analysis. Ecology. https://doi.org/10.1890/0012-9658(2000)081%5b3178:cartap%5d2.0.co;2.CrossRefGoogle Scholar
- Fathian, M., Jouzdani, J., Heydari, M., & Makui, A. (2018). Location and transportation planning in supply chains under uncertainty and congestion by using an improved electromagnetism-like algorithm. Journal of Intelligent Manufacturing,29(7), 1447–1464. https://doi.org/10.1007/s10845-015-1191-9.CrossRefGoogle Scholar
- Hastie, T., Tibshirani, R., & Friedman, J. (n.d.). The elements of statistical learning data mining, inference, and prediction (2nd ed.). Springer Series in Statistics, 2009. Retrieved January 30, 2019 from https://web.stanford.edu/~hastie/Papers/ESLII.pdf.
- Kang, H. S., Lee, J. Y., Choi, S., Kim, H., Park, J. H., Son, J. Y., et al. (2016). Smart manufacturing: Past research, present findings, and future directions. International Journal of Precision Engineering and Manufacturing - Green Technology,3(1), 111–128. https://doi.org/10.1007/s40684-016-0015-5.CrossRefGoogle Scholar
- Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th international joint conference on artificial intelligence (Vol. 2, pp. 1137–1143). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc. http://dl.acm.org/citation.cfm?id=1643031.1643047.
- Mohammadi, H., Farahani, F. V., Noroozi, M., & Lashgari, A. (2017). Green supplier selection by developing a new group decision-making method under type 2 fuzzy uncertainty. International Journal of Advanced Manufacturing Technology,93(1–4), 1443–1462. https://doi.org/10.1007/s00170-017-0458-z.CrossRefGoogle Scholar
- Passino, K. M., & Yurkovich, S. (1998). Fuzzy control. Menlo Park: Addison-Wesley.Google Scholar
- Xu, W., Song, D., & Roe, M. (2011). Production and raw material ordering management for a manufacturing supply chain with uncertainties. In IEEE international conference on industrial engineering and engineering management, (pp. 747–751). https://doi.org/10.1109/ieem.2011.6118016.