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Simulation and emulation of manufacturing systems behaviour

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Current manufacturing systems must operate under resource constraints. Simulation can help to improve the operation of such manufacturing systems. This paper describes the principles of computer simulation and emulation used for the modelling of behaviour of real production systems. Simulation as the technology of 21. Century is used for the analysis of dynamics of manufacturing system. Emulation approach is used for the analysis of a control system and control rules used for the control of a real manufacturing system. The evolution principles and algorithms were used by the optimization of manufacturing system performance. Especially Genetic Algorithms (GA) were used to optimize the parameters of manufacturing system. The paper presents the comparison of results gained trough classical optimization algorithms and GA.
Twórcy
autor
autor
  • University of Žilina, Institute of Competitiveness and Innovation, Univerzitna 1, 010 26 Žilina, Slovakia, phone: 041-5139200, milan.gregor@fstroj.uniza.sk
Bibliografia
  • [1] Plinta D., Modelling and Simulation of the production processes in the conditions of the group working of machine elements, PhD. Thesis, University of Žilina, p. 156, 2001.
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  • [3] Košturiak J., Gregor M., Simulation von Produktionssystemen, Springer Verlag, Wien, 1995.
  • [4] Mishra P.K., Pandey P.C., Simulation Studies of Flexible Manufacturing Systems Using Statistical Design of Experiments, Computers & Industrial Engineering, 16, 1, 65-74, 1989.
  • [5] Gregor M. et al., Simulation of Manufacturing Systems, Publishing House of Lodz University of Technology branch in Bielsko-Biala, Bielsko-Biała, p. 134, 1998.
  • [6] Agarwal G., Babu S.S., A Simulation Study of Performance of MRP Based Systems Under Interaction Effects, Production Planning and Control, 5, 1, 30-46, 1994.
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  • [8] Furmann R., Krajčovič M., Interactive 3D Design of Production Systems, In: Digital Factory 2009 - Workshop Handbook, SLCP, Žilina, p. 28, 2009.
  • [9] Fu C.M., Optimization for Simulation: Theory vs. Practice, INFORMS, 14, 3, 192–215, 2002.
  • [10] Banks J., Carson J.S., Nelson B.L., Nicol D.M., Discrete Event Systems Simulation, 3rd edition, Englewood Clifis, 2000.
  • [11] Barczik R., Distribution Logistics, Publishing House of Lodz University of Technology branch in BielskoBiala, Bielsko-Biała, 2003.
  • [12] Gregor M., Medvecký S., Miieta B., Zilina Intelligent Manufacturing System (ZIMS), CEIT-S001- 05-2010, pp. 50, 2010.
  • [13] Knosala R., AI Method for Production Engineerring, WNT, Warsaw, 2002.
  • [14] Law A.M., Kelton W.D., Simulation Modelling and Analysis, Second Edition, McGraw-Hill, p. 759, ISBN 0-07-100803-9, 1991.
  • [15] Matuszek J., Production Engineering, (in Polish) Publishing House of Lodz University of Technology branch in Bielsko-Biala, Bielsko-Biała, 2000.
  • [16] Michulek T., Using virtual reality to develop six legged walking robot control system, CEIT-S001-04- 2009, p. 37, 2009.
  • [17] Mitchell M., An introduction to genetic algorithms, Cambridge, MA: MIT Press, 1996.
  • [18] Mleczko J., Computer Aided Manufacturing Management, Publishing House of Innovative Technologies Centre Foundation (in Polish), Bielsko-Biała, 2008.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAR0-0065-0017
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