Explicit consideration of multiple objectives in cellular manufacturing

Engineering Optimization

Volumn 39, Issue 5, 2007, Pages 551-565

  Dimopoulos, C ^a  

^a CYPRUS COLLEGE   (Cyprus)

Author keywords

Cell formation problem; Cellular manufacturing; Evolutionary algorithms; Genetic programming; Multi objective optimization; NSGA

Indexed keywords

CLUSTER ANALYSIS; EVOLUTIONARY ALGORITHMS; GENETIC PROGRAMMING; INDUSTRIAL APPLICATIONS; MULTIOBJECTIVE OPTIMIZATION; PROBLEM SOLVING;

CELL FORMATION PROBLEM; SINGLE LINKAGE CLUSTER ANALYSIS (SLCA);

CELLULAR MANUFACTURING;

EID: 34347220551     PISSN: 0305215X     EISSN: 10290273     Source Type: Journal    
DOI: 10.1080/03052150701351631     Document Type: Conference Paper

References (30)

1. Banzhaf, W., Nordin, P., Keller, R.E. and Francone, F.D., Genetic Programming: An Introduction, 1998 (Morgan Kaufman: San Francisco, CA).
2. Boctor, F.F., The minimum cost machine-part cell formation problem. Int. J. Prod. Res., 1996, 34(4), 1045-1063.
3. Burbidge, J.L., The Introduction of Group Technology, 1975 (John Wiley: New York).
4. Chandrasekharan, M.P. and Rajagopalan, R., ZODIAC - an algorithm for concurrent formation of part families and machine-cells. Int. J. Prod. Res., 1987, 25(6), 835-850.
5. Coello Coello, C.A., Evolutionary multiobjective optimization: a historical view of the field. IEEE Comput. Intell. Mag., 2006, 1(1), 28-36.
6. Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput., 2002, 6(2), 182-197.
7. Dimopoulos, C., A review of evolutionary multiobjective optimization applications in the area of production research. In Congress on Evolutionary Computation (CEC '04), pp. 1487-1494, 2004.
8. Dimopoulos, C. and Mort, N., A hierarchical clustering methodology based on genetic programming for the solution of simple cell-formation problems. Int. J. Prod. Res., 2001, 39(1), 1-19.
9. Dimopoulos, C. and Mort, N., Evolving knowledge for the solution of clustering problems in cellular manufacturing. Int. J. Prod. Res., 2004, 42(19), 4119-4133.
10. Eiben, A.E. and Smith, J.E., Introduction to Evolutionary Computing, 2003 (Springer-Verlag: Berlin).
11. Fonseca, C.M. and Fleming, P.J., Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In Fifth International Conference on Genetic Algorithms and their Applications, pp. 416-423, 1993.
12. Gravel, M., Nsakanda, A.L. and Price, W., Efficient solutions to the cell-formation problem with multiple routings via a double-loop genetic algorithm. Eur. J. Oper. Res., 1998, 109, 286-298.
13. Gupta, Y., Gupta, M., Kumar, A. and Sundaram, C., A genetic algorithm-based approach to cell-composition and layout design problems. Int. J. Prod. Res., 1996, 34(2), 447-482.
14. Ho, Y.-C. and Moodie, C.L., Solving cell formation problems in a manufacturing environment with flexible processing and routing capabilities. Int. J. Prod. Res., 1996, 34(10), 2901-2923.
15. Joines, J.A., Culbreth, C.T. and King, R.E., Manufacturing cell design: an integer programming model employing genetic algorithms. IIE Trans., 1996, 28(1), 69-85.
16. King, J.R. and Nakornchai, V., Machine-component group formation in group technology: review and extension. Int. J. Prod. Res., 1982, 20(2), 117-133.
17. Koza, J.R., Genetic Programming: On the Programming Of Computers by Means of Natural Selection, 1992 (MIT Press: Cambridge, MA).
18. Lee, H. and Garcia-Diaz, A., A network flow approach to solve clustering problems in group technology. Int. J. Prod. Res., 1993, 31(3), 603-612.
19. Lin, L.T., Dessouky, M.M., Kumar, K.R. and Ng, M.S., A heuristic-based procedure for the weighted production-cell-formation problem. IIE Trans., 1996, 28(7), 579-589.
20. McAuley, J., Machine grouping for efficient production. Prod. Eng., 1972, 51(2), 53-57.
21. Mansouri, S.A., Moattar Husseini, S.M. and Newman, S.T., A review of the modern approaches to multi-criteria cell design. Int. J. Prod. Res., 2000, 38(5), 1201-1218.
22. Schaffer, J.D., Multiple objective optimization with vector evaluated genetic algorithms. PhD thesis, Vanderbilt University, 1984.
23. Selim, M.H., Askin, R.G. and Vakharia, A.J., Cell formation in group technology: review, evaluation and directions for future research. Comput. Indust. Eng., 1998, 34(1), 3-20.
24. Solimanpur, M., Vrat, P. and Shankar, R., A multi-objective genetic algorithm approach to the design of cellular manufacturing systems. Int. J. Prod. Res., 2004, 42(7), 1419-1441.
25. Srinivas, N. and Deb, K., Multiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput., 1994, 2(3), 221-248.
26. Venugopal, V. and Narendran, T.T., A genetic algorithm approach to the machine-component grouping problem with multiple objectives. Comput. Indust. Eng., 1992, 22(4), 469-480.
27. Wemmerlov, U. and Johnson, D.J., Empirical findings in manufacturing cell design. Int. J. Prod. Res., 2000, 38, 481-507.
28. Zhao, C. and Wu, Z., A genetic algorithm for manufacturing cell-formation with multiple routes and multiple objectives. Int. J. Prod. Res., 2000, 38(3), 385-395.
29. Zitzler, E. and Thiele, L., Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput., 1999, 3(4), 257-271.
30. Zitzler, E. and Thiele, L., SPEA2: improving the performance of the strength Pareto evolutionary algorithm. Tech. Rep. 103, Computer Engineering and Communication Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich, 2001.