We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A non-linear programming model for the machine grouping and a genetic algorithm based solution methodology

by C R Shiyas, V Madhusudanan Pillai
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 38 - Number 8
Year of Publication: 2012
Authors: C R Shiyas, V Madhusudanan Pillai
10.5120/4710-6877

C R Shiyas, V Madhusudanan Pillai . A non-linear programming model for the machine grouping and a genetic algorithm based solution methodology. International Journal of Computer Applications. 38, 8 ( January 2012), 37-41. DOI=10.5120/4710-6877

@article{ 10.5120/4710-6877,
author = { C R Shiyas, V Madhusudanan Pillai },
title = { A non-linear programming model for the machine grouping and a genetic algorithm based solution methodology },
journal = { International Journal of Computer Applications },
issue_date = { January 2012 },
volume = { 38 },
number = { 8 },
month = { January },
year = { 2012 },
issn = { 0975-8887 },
pages = { 37-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume38/number8/4710-6877/ },
doi = { 10.5120/4710-6877 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:24:22.039724+05:30
%A C R Shiyas
%A V Madhusudanan Pillai
%T A non-linear programming model for the machine grouping and a genetic algorithm based solution methodology
%J International Journal of Computer Applications
%@ 0975-8887
%V 38
%N 8
%P 37-41
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cellular Manufacturing is an important application of group technology principles and is suitable in a medium variety medium volume production environment. It is concerned with the production of part types in a flow line manner by dividing the production system into manufacturing cells. In cellular manufacturing system (CMS) design, cell formation is one of the most important steps which contain identification of machine cells and part families. Usually, minimization of intercell movements is the criteria for CMS design. In this paper, we introduce a heterogeneity concept which indicates diversity of machines in a cell and it is measured based on the machine assigned to a cell and machines required for processing of parts visiting the cell. A non-linear integer programming model for the design of manufacturing cells is proposed in this paper to minimize the heterogeneity of cells formed for the given part-machine incidence matrix. The solution is found through a heuristic procedure based on a genetic algorithm coded in MATLAB. The approach produced solution with a grouping efficacy equal to or better than some of the previous approaches based on seven problems.

References
  1. King, J. R., 1980. Machine-component grouping in production flow analysis; an approach using rank order clustering algorithm. International Journal of Production Research, 18, 2, 213–232.
  2. Goncalves, J. F. and Resende, M.G.C., 2004. An evolutionary algorithm for manufacturing cell formation. Computers & Industrial Engineering, 47, 247–273.
  3. Nair G. J. and Narendran T. T., 1998. CASE: A clustering algorithm for cell formation with sequence data; International Journal of Production Research, 36, 157–179.
  4. Wicks, E. M. and Reasor, R. J., 1999. Designing cellular manufacturing systems with dynamic part Populations. IIE Transactions, 31, 11–20.
  5. Pillai, V.M. and Subbarao, K.A., 2008. Robust cellular manufacturing system design for dynamic part population using a genetic algorithm. International Journal of Production Research, 46 ,1, 5191 –5210.
  6. Defersha, F. M. and Chen, M., 2008. A parallel genetic algorithm for dynamic cell formation in cellular manufacturing systems. International Journal of Production Research, 46,22, 6389–6413.
  7. Shang, J. S. and Tadikamalla, P. R., 1998. Multicriteria design and control of a cellular manufacturing system through simulation and optimization. International Journal of Production Research, 36 ,6, 1515–1528.
  8. McCormick, W. T. Scweitzer, P. J. and White, T. W., 1972. Problem decomposition and data reorganization by a cluster technique. Operations Research, 20, 993-1009.
  9. McAuley, J., 1972. Machine grouping for efficient production. Production Engineer, 51, 2,.53–57.
  10. Seifoddini, H. & Wolfe, P. M., 1986. Application of the similarity coefficient method in group technology. IIE Transactions, 18, 3, 266-270.
  11. Srinivasan, G., and Narendran, T. T, 1991. GRAFICS—A nonhierarchical clustering-algorithm for group technology. International Journal of Production Research, 29, 3, 463–478.
  12. Chandrasekharan, M. P. and Rajagopalan, R., 1987. ZODIAC—An algorithm for concurrent formation of part families and machine cells. International Journal of Production Research, 25, 6, 835–850.
  13. Askin, R. G. and Chiu, K. S., 1990. A graph partitioning procedure for machine assignment and cell formation in group technology. International Journal of Production Research, 28, 555-1572.
  14. Choobineh, F., 1988. A framework for the design of cellular manufacturing systems. International Journal of Production Research, 26(7), 1161–1172.
  15. Srinivasan, G., Narendran, T. T. and Mahadevan, B., 1990. An assignment model for the part-families problem in group technology. International Journal of Production Research, 28, l, 145–152.
  16. Adil, G. K., Rajamani, D. and Strong, D., 1997. Assignment allocation and simulated annealing algorithms for cell formation. lIE Transactions, 29, 53-67.
  17. Yasuda, K., Hu, L. and Yin, Y., 2005. A grouping genetic algorithm for the multi-objective cell formation problem. International Journal of Production Research, 43, 4, 829–853.
  18. Adenso-Diaz, B. and Lozano, S., 2008. A model for the design of dedicated manufacturing cells. International Journal of Production Research, 46 ,2, 301–319.
  19. Chen, C. L, Cotruvo, N. A. and Baek,W., 1995. A simulated annealing solution to the cell formation problem. International Journal of Production Research, 33, 2601-2614.
  20. Singh, N. and Rajamani, D., 1996 Cellular Manufacturing Systems Design, Planning and Control, Chapman & Hall: London, UK
  21. Ephsibah, E. P., 2010, Cost Effective Approach on Feature Selection using Genetic Algorithms and LS-SVM Classifier, International journal of computer applications, IJCA Special Issue on “Evolutionary Computation for Optimization Techniques”
  22. Gupta, R. M. and Tompkins, J. A., 1982. An examination of the dynamic behavior of part families in group technology, International Journal of Production Research, 20, 73–86.
  23. Kumar, K. R., & Chandrasekharan, M. P.,1990. Grouping efficacy: A quantitative criterion for goodness of block diagonalforms of binary matrices in group technology. International Journal of Production Research, 28, 2, 233–243.
  24. Cheng, C. H., Gupta, Y. P., Lee, W. H., & Wong, K. F., 1998. A TSP-based heuristic for forming machine groups and part families. International Journal of Production Research, 36, 5, 1325–1337.
  25. Dimopoulos, C., & Mort, N., 2001. A hierarchical clustering methodology based on genetic programming for the solution of simple cell-formation problems. International Journal of Production Research, 39, 1, 1–19.
  26. Dolabi, S. H. H., Hojabri, H., Alagheband, S. A. S., Jaafari, A. A. and Davoudpour, H., 2009. Two phase approach for solving cell formation problem in cellular manufacturing, Proceedings of the World Congress on Engineering and Computer Science, October 20-22, 2009, San Francisco, USA.
Index Terms

Computer Science
Information Sciences

Keywords

Cellular manufacturing systems Heterogeneity of cells Part-machine incidence matrix Genetic algorithm

Powered by PhDFocusTM