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Utilisation of Evolution Algorithm in Production Layout Design

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The need for flexibility of layout planning puts higher requirements for uti-lisation of layout and location problem solving methods. Classical methods, like linear programming, dynamic programming or conventional heuristics are being replaced by advanced evolutionary algorithms, which give better solutions to large-scale problems. One of these methods are also genetic algorithms. This article describes the genetic algorithm utilisation in the production layout planningunder the terms of the digital factory concept.
Rocznik
Strony
5--18
Opis fizyczny
Bibliogr. 20 poz., fig.
Twórcy
  • Department of Industrial Engineering, Faculty of Mechanical Engineering, University of Zilina, Slovakia
autor
  • Department of Industrial Engineering, Faculty of Mechanical Engineering, University of Zilina, Slovakia
Bibliografia
  • [1] Altuntas, S., & Selim, H. (2012). Facility Layout using Weighted Association Rule-based Data Mining Algorithms: Evaluation with Simulation. Expert Systems with Applications, 39(1), 2012, 3–13. doi: 10.1016/j.eswa.2011.06.045
  • [2] Dulina, L., & Bartánusová, M. (2014). Ergonomics in Practice and its Influence on Employees’ Performance. Communications - Scientific Letters of the University of Zilina, 16(3), 206–211.
  • [3] Ďurica, L., Mičieta, B., Bubeník, P., & Biňasová, V. (2015). Manufacturing multi-agent system with bio-inspired techniques: CODESA-Prime. MM science journal, December 2015, 829–837. doi:10.17973/MMSJ.2015_12_201543
  • [4] Furmann, R., & Štefánik, A.(2011).Progressive Solutions Supporting Manufacturing and Logistics Systems Design Developed by CEIT SK, s.r.o. (in Slovak). Produktivita a inovácie: bimonthly magazine of University of Žilina in cooperation with the Slovak productivity center and the Institute for Competitiveness and Innovation, 12(2), 3–5.
  • [5] Gašová, M., Gašo, M., & Štefánik, A. (2017). Document Advanced Industrial Tools of Ergo-nomics Based on Industry 4.0 Concept. Procedia Engineering, 192, 219–224. doi:10.1016/j.proeng.2017.06.038
  • [6] Hančinský, V., & Krajčovič, M. (2014). Genetic Algorithms and their Utilization in Production Scheduling (in Slovak). In Průmyslové inženýrství 2014. International student scientific conference, Kouty nad Desnou: SmartMotion (pp. 49–55).
  • [7] Hnát, J. (2012).Virtual Factory Framework. In Industrial Engineering Moves the World – InvEnt 2012 (pp. 56–59). Zilina: University of Zilina.
  • [8] Krajčovič, M. (2011). Modern Approaches of Manufacturing and Logistics Systems Design (in Slovak). IN Digitalny podnik – cesta k buducnosti zbornik prednasok: CEIT SK, 2011.
  • [9] Krajčovič, M., & Hančinský, V. (2015). Production layout planning using genetic algorithms. Communications : scientific Letters of the University of Žilina, 17(3), 72–77.
  • [10] Krajčovič, M., Bulej, V., Sapietova, A., & Kuric, I. (2013). Intelligent Manufacturing Systems in Concept of Digital Factory. Communications - Scientific Letters of the University of Zilina, 15(2), 77–87.
  • [11] Li, J., & Meerkov, S. M. (2009). Production Systems Engineering. New York: Springer.
  • [12] Mičieta, B., Biňasová, V., & Haluška, M.(2014). The Approaches of Advanced Industrial Engineering in Next Generation Manufacturing Systems. Communications – Scientific Letters of the University of Zilina, 16(3), 101–105.
  • [13] Mičieta, B., Dulina, Ľ., Malcho, M. (2005). Main factors of the selection jobs for the work study. In: Annals of DAAAM for 2005 & Proceedings of the 16th International DAAAM Symposium: Manufacturing & automation: Focus on young researches and scientists (pp. 249–250). Vienna: DAAAM International.
  • [14] Misola, M. G., & Navarro, B. B. (2013). Optimal Facility Layout Problem Solution using Genetic Algorithm. International Journal of Mechanical, Industrial Science and Engineering, 7(8), 622–627.
  • [15] Rakyta, M., Fusko, M., Herčko, J., Závodská, L., & Zrnić, N. (2016). Proactive approach to smart maintenance and logistics as a auxiliary and service processes in a company. Journal of Applied Engineering Science, 14(4), 433–442.
  • [16] Saleh, N. F. B., & Hussain, A. R. B. (2013, October). Genetic Algorithms for Optimizing Manufacturing Facility Layout. Retrieved from http://comp.utm.my/pars/files/2013/04/Genetic-Algorithms-for-Optimizing-Manufacturing-Facility-Layout.pdf
  • [17] Strapek, M., Hořejší, P., & Polcar, J. (2016). 3D laser scanned data processing possibilities for production floors models. IN Proceedings of the 28th International Business Information Management Association Conference (pp. 2920–2930). Norristown: International Business Information Management Association
  • [18] Trebuňa, P., Kliment, M., Edl, M., & Petrik, M. (2014). Creation of simulation model of expan-sion of production in manufacturing companies. Procedia Engineering, 96, 477–482.
  • [19] Xu, L., Yang, S., Li, A., & Matta, A. (2011). An Adaptive Genetic Algorithm for Facility Problem in Cylinder Block Line. In Computer Science and Automation Engineering (CSAE), 2011 IEEE International Conference (pp. 749–753). IEEE. doi: 10.1109/CSAE.2011.5952782
  • [20] Yang, T., Zhang, D., Chen, B., & Li, S. (2008). Research on plant layout and production line running simulation in digital factory environment. In Pacific-Asia workshop on compu-tational intelligence and industrial application (vol. 2, pp. 588–593). IEEE. doi: 10.1109/PACIIA.2008.159
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-35e8e7b4-cfbc-40be-801b-f157232e9c56
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