Abstract
Models of artificial neural networks can be used to control a production system, and thus to ensure its stability. Such models are very useful tools, because they can be built quickly and easily. The paper presents the possibility to use an artificial neural network for forecasting the production volume. A production plant manufacturing flywheels was used as an example. The specific character of the plant consisting in a limited access to production resources and the need for timely execution of variable customer orders caused that an ANN model built in the SAS Enterprise Miner 6.2 environment was used to solved this problem. This paper presents the manner of building the ANN and the results of the experiments that consisted in forecasting the production volumes depending on the values of independent variables.
This is a preview of subscription content, log in via an institution to check access.
References
Antosz, K., Stadnicka, D.: The results of the study concerning the identification of the activities realized in the management of the technical infrastructure in large enterprises. Maint. Reliab. 16(1), 112–119 (2014)
Bożejko, W., Rajba, P., Wodecki, M.: Scheduling problem with uncertain parameters in Just in Time system. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) Artificial Intelligence and Soft Computing. Lecture Notes in Artificial Intelligence, vol. 8468. Springer, Cham (2014)
Burduk, A.: The role of artificial neural network models in ensuring the stability of systems. In: Herrero, Á., Sedano, J., Baruque, B., Quintián, H., Corchado, E. (eds.) 10th International Conference on Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol. 368. Springer, Cham (2015)
Burduk, A.: Artificial neural networks as tools for controlling production systems and ensuring their stability. In: Saeed, K., Chaki, R., Cortesi, A., Wierzchoń, S. (eds.) Computer Information Systems and Industrial Management. Lecture Notes in Computer Science, vol. 8104. Springer, Heidelberg (2013)
Chlebus, E., Helman, J., Olejarczyk, M., Rosienkiewicz, M.: A new approach on implementing TPM in a mine - case study. Arch. Civil Mech. Eng. 15(4), 873–884 (2015)
Grzybowska, K., Kovács, G.: The modelling and design process of coordination mechanisms in the supply chain. J. Appl. Logic. (2016)
Grzybowska, K.: Selected activity coordination mechanisms in complex systems. In: Bajo, J., et al. (eds.) Highlights of Practical Applications of Agents, Multi-Agent Systems, and Sustainability - The PAAMS Collection. Communications in Computer and Information Science, vol. 524. Springer, Cham (2015)
Jasiulewicz-Kaczmarek, M.: Practical aspects of the application of RCM to select optimal maintenance policy of the production line. In: Safety and Reliability: Methodology and Applications, Proceedings of the European Safety and Reliability Conference (ESREL), pp. 1187–1195 (2015)
Kłos, S., Skrzypek, K., Dąbrowski, K.: ERP-based innovation management system for engineering-to-order production. In: Innovation Management, Development Sustainability, and Competitive Economic Growth - Vision 2020, International Business Information Management Association (IBIMA), Seville, Spain, pp. 3007–3016 (2016)
Kłosowski, G., Gola, A., Świć, A.: Application of Fuzzy Logic in assigning workers to production tasks. In: Omatu, S., et al. (eds.) Distributed Computing and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol. 474, pp. 505–513. Springer, Cham (2016)
Krenczyk, D., Kalinowski, K., Grabowik, C.: Integration production planning and scheduling systems for determination of transitional phases in repetitive production. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, S.B. (eds.) Hybrid Artificial Intelligent Systems, vol. 7209, pp. 274–283. Springer, Heidelberg (2012)
Loska, A.: Exploitation assessment of selected technical objects using taxonomic methods. In: Eksploatacja i Niezawodnosc – Maintenance and Reliability, vol. 15, No. 1, pp. 1–8 (2013)
Mazurkiewicz, D.: Computer-aided maintenance and reliability management systems for conveyor belts. In: Eksploatacja i Niezawodnosc – Maintenance and reliability, vol. 16, No. 3, pp. 377–382 (2014)
Rojek, I., Studzinski, J.: Comparison of different types of neuronal nets for failures location within water-supply networks. In: Eksploatacja i Niezawodność – Maintenance and Reliability, vol. 16 (1), pp. 42–47 (2014)
Wieczorek, T.: Neural Models of Technological Processes, Monograph. Publishing House of the Silesian University of Technology, Gliwice (2008)
Tung-Hsu, H., Wang-Lin, L., Li, L.: Intelligent remote monitoring and diagnosis of manufacturing processes using an integrated approach of neural networks and rough sets. J. Intell. Manuf. 18(2), 239–253 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Burduk, A., Chlebus, T., Waszkowski, R. (2018). Assessment of the Feasibility of a Production Plan with the Use of an Artificial Neural Network Model. In: Burduk, A., Mazurkiewicz, D. (eds) Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017. ISPEM 2017. Advances in Intelligent Systems and Computing, vol 637. Springer, Cham. https://doi.org/10.1007/978-3-319-64465-3_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-64465-3_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-64464-6
Online ISBN: 978-3-319-64465-3
eBook Packages: EngineeringEngineering (R0)