Advertisement

Determining an Efficient Parts Layout for Assembly Cell Production by Using GA and Virtual Factory System

  • Hidehiko Yamamoto
  • Takayoshi Yamada
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 10)

Abstract

This paper describes a system that can determine an efficient parts layout for assembly cell production before setting up a real cell production line in a factory. This system is called the Virtual Assembly Cell production System (VACS). VACS consists of two modules, a genetic algorithm (GA) for determining the parts layout and a virtual production system. The GA system utilizes a unique crossover method called Twice Transformation Crossover. VACS is applied to a cell production line for assembling a personal computer. An efficient parts layout is generated, which demonstrates the usefulness of VACS.

Keywords

Production System Virtual Factory Assembly Line Cell Production GA 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zhang, J., Chan, F.T.S., et al.: Investigation of the reconfigurable control system for an agile manufacturing cell. International Journal of Production Research 40(15), 3709–3723 (2002)CrossRefGoogle Scholar
  2. 2.
    Solimanpur, M., Vrat, P., Shankar, R.: A multi-objective genetic algorithm approach to the design of cellular manufacturing systems. International Journal of Production Research 42(7), 1419–1441 (2004)zbMATHCrossRefGoogle Scholar
  3. 3.
    Inukai, T., et al.: Simulation Environment Synchronizing Real Equipment for Manufacturing Cell. Journal of Advanced Mechanical Design, Systems, and Manufacturing, The Japan Society of Mechanical Engineers 1(2), 238–249 (2007)CrossRefGoogle Scholar
  4. 4.
    Yamamoto, H.: One-by-One Parts Input Method by Off-Line Production Simulator with GA. European Journal of Automation, Hermes Science Publications, Artiba, A. (ed.), 1173–1186 (2000)Google Scholar
  5. 5.
    Yamamoto, H., Marui, E.: Off-line Simulator to Decide One-by-one Parts Input Sequence of FTL—Method of Keep Production Ratio by Using Recurring Individual Expression. Journal of the Japan Society for Precision Engineering 69(7), 981–986 (2003)CrossRefGoogle Scholar
  6. 6.
    Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  7. 7.
    Yamamoto, H.: One-by-one Production Planning by Knowledge Revised-Type Simulator with GA. Transactions of the Japan Society of Mechanical Engineers, Series C 63(609), 1803–1810 (1997)Google Scholar
  8. 8.
    Ong, S.K., Ding, J., Nee, A.Y.C.: Hybrid GA and SA dynamic set-up planning optimization. International Journal of Production Research 40(18), 4697–4719 (2002)zbMATHCrossRefGoogle Scholar
  9. 9.
    Yamamoto, H., Qudeiri, J.A., Yamada, T., Ramli, R.: Production Layout Design System by GA with One by One Encoding Method. The Journal of Artificial Life and Robotics 13(1), 234–237 (2008)CrossRefGoogle Scholar
  10. 10.
    Qiao, L., Wang, X.-Y., Wang, S.-C.: A GA-based approach to machining operation sequencing for prismatic parts. International Journal of Production Research 38(14), 3283–3303 (2000)zbMATHCrossRefGoogle Scholar
  11. 11.
    Fanjoy, D.W., Crossley, W.A.: Topology Design of Planar Cross-Sections with a Genetic Algorithm: Part 1—Overcoming the Obstacles. International Journal of Engineering Optimization 34(1), 1–22 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hidehiko Yamamoto
    • 1
  • Takayoshi Yamada
    • 1
  1. 1.Department of Human and Information SystemGifu UniversityJapan

Personalised recommendations