Title

Comparison of the Classical Algorithm with the Training Algorithm in Scheduling Problem ADI Production

Journal title

Archives of Foundry Engineering

Yearbook

2022

Volume

vol. 22

Issue

No 1

Affiliation

Wilk-Kołodziejczyk, D. : AGH University of Science and Technology, Al. A. Mickiewicza 30, 30-059 Krakow, Poland ; Wilk-Kołodziejczyk, D. : Łukasiewicz Research Network – Krakow Institute of Technology, 73 Zakopiańska Str., 30-418 Kraków, Poland ; Chrzan, K. : Łukasiewicz Research Network – Krakow Institute of Technology, 73 Zakopiańska Str., 30-418 Kraków, Poland ; Jaśkowiec, K. : Łukasiewicz Research Network – Krakow Institute of Technology, 73 Zakopiańska Str., 30-418 Kraków, Poland ; Pirowski, Z. : Łukasiewicz Research Network – Krakow Institute of Technology, 73 Zakopiańska Str., 30-418 Kraków, Poland ; Żuczek, R. : Łukasiewicz Research Network – Krakow Institute of Technology, 73 Zakopiańska Str., 30-418 Kraków, Poland ; Bitka, A. : Łukasiewicz Research Network – Krakow Institute of Technology, 73 Zakopiańska Str., 30-418 Kraków, Poland ; Machulec, D. : AGH University of Science and Technology, Kraków, Poland

Authors

Wilk-Kołodziejczyk, D. ; Chrzan, K. ; Jaśkowiec, K. ; Pirowski, Z. ; Żuczek, R. ; Bitka, A. ; Machulec, D.

Keywords

production scheduling ; Austempered ductile iron ; Iron castings ; Foundry industry ; Training algorithm ; Tabu search

Divisions of PAS

Nauki Techniczne

Coverage

5-12

Publisher

The Katowice Branch of the Polish Academy of Sciences

Bibliography

[1] Yang, L., Jiang, G., Chen, X., Li, G., Li, T. & Chen, X. (2019). Design of integrated steel production scheduling knowledge network system. Claster Comput. 10197-10206.
[2] Żurada, J. Barski, M., Jędruch, W. (1996). Artificial Neural Networks. Fundamentals of theory and application. Warszawa: PWN. (in Polish).
[3] Janiak, A. (2006). Scheduling in computer and manufacturing systems. Warszawa: Wydawnictwa Komunikacji i Łączności.
[4] Smutnicki, C. (2002). Scheduling algorithms. Warszawa: Akademicka Oficyna Wydawnicza EXIT. (in Polish).
[5] Coffman, E.G. (1980). Task scheduling theory. Warszawa: Wydawnictwa Naukowo-Techniczne. (in Polish).
[6] Janczarek, M. (2011). Managing production processes in the enterprise. Lublin: Lubelskie Towarzystwo Naukowe. (in Polish).
[7] Szeliga, M. (2019) Practical machine learning. Warszawa: PWN. (in Polish).
[8] Raschka, S. (2018) Python machine learning. Gliwice: Helion. (in Polish).
[9] Choi, H-S, Kim, J-S. & Lee, D-H. (2011). Real-time scheduling for reentrant hybrid flow shops: A decision tree based mechanism and its application to a TFT-LCD line. Expert System with Application. 38, 3514-3521.
[10] Agarwal, A., Pirkul, H. & Jacob, V.S. (2003). Augmented neutral network for task scheduling. European Journal of Operational Research. 151, 481-502.
[11] Jain, A.S. & Meeran, S. (1998). Jop-shop scheduling using neutral networks. International Journal of Production Research. 36(5), 1249-1272
[12] Fonseca-Reyna, Y.C., Martinez-Jimenez, Y. & Nowe, A. (2017). Q-Learning algorithm performance for m-machine, n-jobs flow shop scheduling problems to minimize makespan, Revista Investigacion Operacional. 38(3), 281-290.
[13] Dewi, Andriansyah, & Syahriza, (2019). Optimization of flow shop scheduling problem using classic algorithm: case study, IOP Conf. Series: Materials Science and Engineering 506.
[14] Putatunda, K. (2001) Development of austempered ductile cast iron (ADI) with simultaneous high yield strength and fracture toughness by a novel two-step austempering process. Material Science and Engineering A. 315, 70-80.
[15] Dayong Han, Hubei Key, Qiuhua Tang; Zikai Zhang; Jun Cao, (2020). Energy-efficient integration optimization of production scheduling and ladle dispatching in steelmaking plants. IEEE Access. 8, 176170-176187.
[16] Perzyk, M. (2017). The use of production data mining methods in the diagnosis of the causes of product defects and disruptions in the production process. Utrzymanie Ruchu. 4, 45-47. (in Polish).
[17] Perzyk, M., Dybowski, B. & Kozłowski, J. (2019). Introducing advanced data analytics in perspective of industry 4.0 in a die casting foundry. Archives of Foundry Engineering. 19(1), 53-57.
[18] Yescas, M. (2003). Prediction of the Vickers hardness in austempered ductile irons using neural networks. International Journal of Cast Metals Research. 15(5), 513-521.
[19] Report on the contract no. U / 227/2014 implemented at the Foundry Research Institute. (in Polish).

Date

2022.01.18

Type

Article

Identifier

DOI: 10.24425/afe.2022.140210