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An integrated artificial neural network-computer simulation for optimization of complex tandem queue systems

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  • Azadeh, A.
  • Faiz, Z.S.
  • Asadzadeh, S.M.
  • Tavakkoli-Moghaddam, R.

Abstract

This paper presents an integrated artificial neural network-computer simulation (ANNSim) for optimization of G/G/K queue systems. The ANNSim is a computer program capable of improving its performance by referring to production constraints, system's limitations and desired targets. It is a goal oriented, flexible and integrated approach and produces the optimum solution by utilizing Multi Layer Perceptron (MLP) neural networks. The properties and modules of the prescribed intelligent ANNSim are: (1) parametric modeling, (2) flexibility module, (3) integrated modeling, (4) knowledge-base module, (5) integrated database and (6) learning module. The integrated ANNSim is applied to 30 distinct tandem G/G/K queue systems. Furthermore, its superiority over conventional simulation approach is shown in two dimensions which are average run time and maximum number of required iterations (scenarios).

Suggested Citation

  • Azadeh, A. & Faiz, Z.S. & Asadzadeh, S.M. & Tavakkoli-Moghaddam, R., 2011. "An integrated artificial neural network-computer simulation for optimization of complex tandem queue systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(4), pages 666-678.
    • Handle: RePEc:eee:matcom:v:82:y:2011:i:4:p:666-678
    DOI: 10.1016/j.matcom.2011.06.009
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    References listed on IDEAS

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    1. Azadeh, A. & Asadzadeh, S.M. & Ghanbari, A., 2010. "An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments," Energy Policy, Elsevier, vol. 38(3), pages 1529-1536, March.
    2. A. Azadeh & M. Haghnevis & Y. Khodadadegan, 2009. "An improved model for production systems with mixed queuing priorities: an integrated simulation, AHP and Value Engineering approach," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 4(5), pages 536-553.
    3. Chen, Serena H. & Jakeman, Anthony J. & Norton, John P., 2008. "Artificial Intelligence techniques: An introduction to their use for modelling environmental systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 379-400.
    4. Chambers, M. & Mount-Campbell, C. A., 2002. "Process optimization via neural network metamodeling," International Journal of Production Economics, Elsevier, vol. 79(2), pages 93-100, September.
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    Cited by:

    1. A. Azadeh & M. S. Naghavi lhoseiny & V. Salehi, 2018. "Optimum alternatives of tandem G/G/K queues with disaster customers and retrial phenomenon: interactive voice response systems," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 68(3), pages 535-562, July.

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