DOI QR코드

DOI QR Code

Machine Layout Decision Algorithm for Cell Formation Problem Using Self-Organizing Map

자기조직화 신경망을 이용한 셀 형성 문제의 기계 배치순서 결정 알고리듬

  • Received : 2019.06.01
  • Accepted : 2019.06.12
  • Published : 2019.06.30

Abstract

Self Organizing Map (SOM) is a neural network that is effective in classifying patterns that form the feature map by extracting characteristics of the input data. In this study, we propose an algorithm to determine the cell formation and the machine layout within the cell for the cell formation problem with operation sequence using the SOM. In the proposed algorithm, the output layer of the SOM is a one-dimensional structure, and the SOM is applied to the parts and the machine in two steps. The initial cell is formed when the formed clusters is grouped largely by the utilization of the machine within the cell. At this stage, machine cell are formed. The next step is to create a flow matrix of the all machine that calculates the frequency of consecutive forward movement for the machine. The machine layout order in each machine cell is determined based on this flow matrix so that the machine operation sequence is most reflected. The final step is to optimize the overall machine and parts to increase machine layout efficiency. As a result, the final cell is formed and the machine layout within the cell is determined. The proposed algorithm was tested on well-known cell formation problems with operation sequence shown in previous papers. The proposed algorithm has better performance than the other algorithms.

Keywords

References

  1. Diaz, J.A., Luna, D., and Luna, R., A GRASP Heuristic for the Manufacturing Cell Formation Problem, Trabajos de Investigacion Operativa, 2012, Vol. 20, Issue 3, pp. 679-706.
  2. Goncalves, J.F. and Resende, M.G.C., An Evolutionary Algorithm for Manufacturing Cell Formation, Computers & Industrial Engineering, 2004, Vol. 47, Issue 2-3, pp. 247-273. https://doi.org/10.1016/j.cie.2004.07.003
  3. Ham, I., Hitomi, K., and Yoshida, T., Group Technology : Production Methods in Manufacture, Kluwer-Nijhoff, Boston, MA, 1985.
  4. Harhalakis, G., Nagi, R., and Proth, J.M., An Efficient Heuristic in Manufacturing Cell Formation for Group Technology Applications, International Journal of Production Research, 1990, Vol. 28, No. 1, pp. 185-198. https://doi.org/10.1080/00207549008942692
  5. Jeon, Y.D. and Kang, M.K., A Self-Organizing Neural Networks Approach to Machine-Part Grouping in Cellular Manufacturing Systems, Journal of Society of Korea Industrial and Systems Engineering, 1998, Vol. 21, No. 48, pp. 123-132.
  6. Jeon, Y.D. and Kang, M.K., Grouping of parts reflecting changes of manufacturing conditions : An algorithm based on the self-organizing neural networks, Journal of the Korean Institute of Plant Engineering, 1998, Vol. 3, No. 2, pp. 241-251.
  7. Jeon, Y.D., Machine-Part Grouping with Alternative Process Plan : An algorithm based on the self-organizing neural networks, Journal of Society of Korea Industrial and Systems Engineering, 2016, Vol. 39, No. 3, pp. 83-89. https://doi.org/10.11627/jkise.2016.39.3.083
  8. Kohonen, T., Self-organized formation of topologically correct feature maps, Biological Cybernetics, 1982, Vol. 43, Issue 1, pp. 59-69. https://doi.org/10.1007/BF00337288
  9. Kumar, C.S. and Chandrasekharan, M.P., Grouping efficacy : A quantitative criterion for goodness of block diagonal forms of binary matrices in group technology, International Journal of Production Research, 1990, Vol. 28, No. 2, pp. 233-243. https://doi.org/10.1080/00207549008942706
  10. Lee, S.U., Machine Layout Decision Algorithm for Cellular Formation Problem, Journal of The Korea Society of Computer and Information, 2016, Vol. 21, No. 4, pp. 47-54. https://doi.org/10.9708/jksci.2016.21.4.047
  11. Mahadavi, I. and Mahadevan, B., CLASS : An Algorithm for Cellular Manufacturing System and Layout Design Using Sequence Data, Robotics and Computer-Integrated Manufacturing, 2008, Vol. 24, Issue 3, pp. 488-497. https://doi.org/10.1016/j.rcim.2007.07.011
  12. Mahadavi, I., Shirazi, B., and Paydar, M.M., A Flow Matrix-based Heuristic Algorithm for Cell Formation and Layout Design in Cellular Manufacturing System, International Journal of Advanced Manufacturing Technology, 2008, Vol. 39, Issue 9-10, pp. 943-953. https://doi.org/10.1007/s00170-007-1274-7
  13. Murugan, M. and Selladurai, V., Formation of Machine Cells/Part Families in Cellular Manufacturing Systems Using an ART-Modified Single Linkage Clustering Approach-A Comparative Study, Jordan Journal of Mechanical and Industrial Engineering, 2011, Vol. 5, No. 3, pp. 199-212.
  14. Mutingi, M. and Onwubolu, G.C., Manufacturing System, Chapter 10. Integrated Cellular Manufacturing System Design and Layout Using Group Genetic Algorithms, Interopen.com, 2012, pp. 205-222.
  15. Nair, G.J. and Narendran, T.T., CASE : A Clustering Algorithm for Cell Formation with Sequence Data, International Journal of Production Research, 1998, Vol. 36, No. 1, pp. 157-179. https://doi.org/10.1080/002075498193985
  16. Nouri, H., Tang, S.H., Tuah, B.T.H., Ariffin, M.K.A., and Samin, R., Metaheuristic Techniques on Cell Formation in Cellular Manufacturing System, Journal of Automation and Control Engineering, 2013, Vol. 1, No. 1, pp. 49-54. https://doi.org/10.12720/joace.1.1.49-54
  17. Tam, K.Y., An Operation Sequence Based Similarity Coefficient for Part Family Formations, Journal of Manufacturing Systems, 1988, Vol. 9, Issue 1, pp. 55-68. https://doi.org/10.1016/0278-6125(90)90069-t
  18. Teymourian, E., Mahadavi, I., and Kayvanfar, V., A New Cell Formation Model Using Sequence Data and Handling Cost Factors, Proceedings of International Conference on Industrial Engineering and Operations Management, 2011, Kuala Lumpur, Malaysia, Jan, 22-24.