An Artificial Neural Network Based Simulation Metamodeling Approach for Dual Resource Constrained Assembly Line

  • Gokalp Yildiz
  • Ozgur Eski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


The main objective of this study is to find the optimum values of design and operational parameters related to worker flexibility in a Dual Resource Constrained (DRC) assembly line considering the performance measures of Hourly Production Rate (HPR), Throughput Time (TT) and Number of Worker Transfers (NWT). We used Artificial Neural Networks (ANN) as a simulation metamodel to estimate DRC assembly line performances for all possible alternatives. All alternatives were evaluated with respect to a utility function which consists of weighted sum of normalized performance measures.


Artificial Neural Network Artificial Neural Network Model Assembly Line Work Flexibility Throughput Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Araz, U.O., Eski, O., Araz, C.: A Multi-Criteria Decision Making Procedure Based on Neural Networks for Kanban Allocation. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 898–905. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Fasihul, M.A., Ken, R.M., Trevor, J.R.: A Comparison of Experimental Designs in the Development of a Neural Network Simulation Metamodel. Simulation Modeling Practice and Theory 12, 559–578 (2004)CrossRefGoogle Scholar
  3. 3.
    Fonseca, D.J., Navaresse, D.O., Moynihan, G.P.: Simulation Metamodeling Through Artificial Neural Networks. Engineering Applications of Artificial Intelligence 16, 177–183 (2003)CrossRefGoogle Scholar
  4. 4.
    Fryer, J.S.: Labor Flexibility in Multiechelon Dual Constrained Job Shops. Management Science 20, 1073–1080 (1974)CrossRefGoogle Scholar
  5. 5.
    Gunther, R.E.: Server Transfer Delays in A Dual Resource Constrained Parallel Queuing System. Management Science 25(12), 1245–1257 (1979)CrossRefGoogle Scholar
  6. 6.
    Hornik, K., Stinchcombe, M.W.H.: Multilayer Feedforward Networks are Universal Approximators. Neural Networks 2, 359–366 (1989)CrossRefGoogle Scholar
  7. 7.
    Hurrion, R.D.: An Example of Simulation Optimization Using A Neural Network Metamodel: Finding the Optimum Number of Kanbans in Manufacturing System. Journal of the Operation Research Society 48, 1105–1112 (1997)MATHGoogle Scholar
  8. 8.
    Kilmer, R., Smith, A., Shuman, L.: An Emergency Department Simulation and a Neural Network Metamodel. Journal of the Society for Health Systems 5(3), 63–79 (1997)Google Scholar
  9. 9.
    Nelson, R.T.: Labor and Machine Limited Production Systems. Management Science 13(9), 648–671 (1967)CrossRefGoogle Scholar
  10. 10.
    Pierreval, H.: Training a Neural Network by Simulation for Dispatching Problems. In: Proceedings of the Third Rensselaer International Conference on Computer Integrated Engineering, pp. 332–336 (1992)Google Scholar
  11. 11.
    Savsar, M., Choueiki, M.H.: A Neural Network Procedure for Kanban Allocation in JIT Production Control Systems. International Journal of Production Research 38, 3247–3265 (2000)MATHCrossRefGoogle Scholar
  12. 12.
    Treleven, M.D.: A Review of the Dual Resource Constrained System Research. IIE Transactions 21(3), 279–287 (1989)CrossRefGoogle Scholar
  13. 13.
    Treleven, M.D.: The Timing of Labor Transfers in Dual Resource-Constrained Systems: Push vs. Pull Rules. Decision Sciences 18(1), 73–88 (1987)CrossRefGoogle Scholar
  14. 14.
    Treleven, M.D., Elvers, D.A.: An Investigation of Labor Assignment Rules in a Dual- Constrained Job Shop. Journal of Operations Management 6(1), 51–67 (1985)CrossRefGoogle Scholar
  15. 15.
    Yıldız, G.: An Application of Typical When/Where Rule Pairs in Dual Resource Constrained (DRC) Assembly Line with Closed-Loop Conveyor. In: Proceedings of 35th International Conference of Computers and Industrial Engineering, pp. 2185–2190 (2005)Google Scholar
  16. 16.
    Zhang, Q., Vonderembse, M.A., Lim, J.S.: Manufacturing Flexibility: Defining and Analyzing Relationships among Competence, Capability, and Customer Satisfaction. Journal of Operations Management 21(2), 173–191 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gokalp Yildiz
    • 1
  • Ozgur Eski
    • 1
  1. 1.Faculty of Engineering, Industrial Engineering Dept.Dokuz Eylul UniversityBornova-IzmirTurkey

Personalised recommendations