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

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

Identyfikatory

DOI

10.24425/afe.2022.140210

• Języki publikacji: EN

Abstrakty

EN

A classical algorithm Tabu Search was compared with Q Learning (named learning) with regards to the scheduling problems in the Austempered Ductile Iron (ADI) manufacturing process. The first part comprised of a review of the literature concerning scheduling problems, machine learning and the ADI manufacturing process. Based on this, a simplified scheme of ADI production line was created, which a scheduling problem was described for. Moreover, a classic and training algorithm that is best suited to solve this scheduling problem was selected. In the second part, was made an implementation of chosen algorithms in Python programming language and the results were discussed. The most optimal algorithm to solve this problem was identified. In the end, all tests and their results for this project were presented.

Słowa kluczowe

EN

production scheduling; austempered ductile iron; iron castings; foundry industry; training algorithm; tabu search

PL

terminarz produkcji; żeliwo sferoidalne; odlewy żeliwne; przemysł odlewniczy

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2022
• Tom: Vol. 22, iss. 1
• Strony: 5--12
• Opis fizyczny: Bibliogr. 19 poz., rys., tab., wykr.

Twórcy

autor

Wilk-Kołodziejczyk D.

• AGH University of Science and Technology, Kraków, Poland
• Łukasiewicz Research Network - Kraków Institute of Technology, Poland

autor

Chrzan K.

• Łukasiewicz Research Network - Kraków Institute of Technology, Poland

autor

Jaśkowiec K.

• Łukasiewicz Research Network - Kraków Institute of Technology, Poland

autor

Pirowski Z.

• Łukasiewicz Research Network - Kraków Institute of Technology, Poland

autor

Żuczek R.

• Łukasiewicz Research Network - Kraków Institute of Technology, Poland

autor

Bitka A.

• Łukasiewicz Research Network - Kraków Institute of Technology, Poland

autor

Machulec D.

• AGH University of Science and Technology, Kraków, Poland

Bibliografia

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• [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).
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• [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.
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• [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).