Genetic algorithm approach for solving a cell formation problem in cellular manufacturing

Expert Systems with Applications

Volumn 36, Issue 3 PART 2, 2009, Pages 6598-6604

  Mahdavi, Iraj ^a   Paydar, Mohammad Mahdi ^a   Solimanpur, Maghsud ^b   Heidarzade, Armaghan ^a,c  

^a DEPARTMENT OF INDUSTRIAL ENGINEERING   (Iran)

^b URMIA UNIVERSITY   (Iran)

^c PAYAME NOOR UNIVERSITY   (Iran)

Author keywords

Cell formation; Cellular manufacturing; Genetic algorithm; Group efficacy; Mathematical model

Indexed keywords

CELLS; CYTOLOGY; GENETIC ALGORITHMS; GROUP TECHNOLOGY; MATHEMATICAL MODELS; NUMERICAL METHODS;

CELL FORMATION; CELL FORMATION PROBLEM; EXCEPTIONAL ELEMENTS; GENETIC ALGORITHM APPROACH; GROUP EFFICACY; INTEGER VARIABLES; NONLINEAR TERMS; OPTIMAL SOLUTIONS;

CELLULAR MANUFACTURING;

EID: 58349114021     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.07.054     Document Type: Article

References (44)

1. Back T., Fogel D., and Michalawecz Z. Handbook of evolutionary computation (1997), Oxford University Press, Oxford
2. Boctor F.F. A linear formulation of the machine-part cell formation problem. International Journal of Production Research 29 2 (1991) 343-356
3. Brown E., and Sumichrast R. CF-GGA: A grouping genetic algorithm for the cell formation problem. International Journal of Production Research 36 (2001) 3651-3669
4. Carrie A.S. Numerical taxonomy applied to group technology and plant layout. International Journal of Production Research 11 4 (1973) 399-416
5. Chan H.M., and Milner D.A. Direct clustering algorithm for group formation in cellular manufacture. Journal of Manufacturing Systems 1 (1982) 65-75
6. Chandrasekharan M.P., and Rajagopalan R. MODROC: An extension of rank order clustering for group technology. International Journal of Production Research 24 5 (1986) 1221-1264
7. Chandrasekharan M.P., and Rajagopalan R. An ideal seed non-hierarchical clustering algorithm for cellular manufacturing. International Journal of Production Research 24 2 (1986) 451-464
8. Chandrasekharan M.P., and Rajagopalan R. ZODIAC: An algorithm for concurrent formation of part-families and machine-cells. International Journal of Production Research 25 6 (1987) 835-850
9. Chandrasekharan M.P., and Rajagopalan R. GROUPABILITY: An analysis of the properties of binary data matrices for group technology. International Journal of Production Research 27 6 (1989) 1035-1052
10. Chen S.J., and Cheng C.S. A neural network based cell formation algorithm in cellular manufacturing. International Journal of Production Research 33 2 (1995) 293-318
11. Cheng C., Gupta Y., Lee W., and Wong K. A TSP-based heuristic for forming machine groups and part families. International Journal of Production Research 36 (1998) 1325-1337
12. Chu, C. H. & Tsai, C. C. (2001). A heuristic algorithm for grouping manufacturing cells. In Proceedings of the 2001 IEEE Congress on Evolutionary Computation, Seoul, Korea (pp. 310-317).
13. Davis L. Handbook of genetic algorithms (1991), Van Nostrand, New York
14. Gen M., and Cheng R. Genetic algorithms and engineering design (1997), Wiley Interscience Publication, MA
15. Goldberg D.E. Genetic algorithms: Search, optimization & machine learning (1989), Addison-Wesley, Inc., MA
16. Goncalves J., and Resende M. An evolutionary algorithm for manufacturing cell formation. Computers and Industrial Engineering 47 (2004) 247-273
17. Heragu S.S. Group technology and cellular manufacturing. IEEE Transactions on Systems, Man and Cybernetics 24 2 (1994) 203-214
18. Holland J.H. Adaptation in natural and artificial system (1975), University of Michigan Press, Ann Arbor, MI
19. Ji, P., Yongzhong, W. & Haozhao. L. (2006). A solution method for the quadratic assignment problem (QAP). Presented at the Sixth International Symposium on Operations Research and Its Applications (ISORA'06), Xinjiang, China, August 2006.
20. Joines J.A., King R.E., and Culbreth C.T. A comprehensive review of production oriented manufacturing cell formation techniques. International Journal of Flexible Automation and Integrated Manufacturing 3 (1996) 225-265
21. Joines J.A., Culbreth C.T., and King R.E. Manufacturing cell design: An integer programming model employing genetic algorithms. IIE Transactions 28 (1996) 69-85
22. King J.R. Machine-component grouping in production flow analysis: An approach using a rank order clustering algorithm. International Journal of Production Research 18 (1980) 213-232
23. Kusiak A., and Chow W.S. Efficient solving of the group technology problem. Journal of Manufacturing Systems 6 2 (1987) 117-124
24. Kusiak A., and Cho M. Similarity coefficient algorithm for solving the group technology problem. International Journal of Production Research 30 11 (1992) 2633-2646
25. Kumar C., and Chandrasekharan M. Grouping efficacy: A quantitative criterion for goodness of block diagonal forms of binary matrices in group technology. International Journal of Production Research 28 (1990) 233-243
26. Mahdavi I., Kaushal O.P., and Chandra M. Graph-neural network approach in cellular manufacturing on the basis of a binary system. International Journal of Production Research 39 13 (2001) 2913-2922
27. Mahdavi I., Javadi B.F., Alipour K., and Slomp J. Designing a new mathematical model for cellular manufacturing system based on cell utilization. Applied Mathematics and Computation 190 (2007) 662-670
28. Man K.F., Tang K.S., and Kwong S. Genetic algorithms: Concepts and design (1999), Springer, London
29. Mosier C.T., and Taube L. Weighted similarity measure heuristics for the group technology machine clustering problem. OMEGA 13 6 (1985) 577-583
30. Onwubolu G.C., and Mutingi M. A genetic algorithm approach to cellular manufacturing systems. Computers and Industrial Engineering 39 1-2 (2001) 125-144
31. Seifoddini H. A note on the similarity coefficient method and the problem of improper machine assignment in group technology applications. International Journal of Production Research 27 7 (1989) 1161-1165
32. Selim H.M., Askin R.G., and Vakharia A.J. Cell formation in group technology: Review, evaluation and directions for future research. Computers and Industrial Engineering 34 (1998) 3-20
33. Singh N. Design of cellular manufacturing systems: An invited review. European Journal of Operational Research 69 (1993) 284-291
34. Soleymanpour M., Vrat P., and Shanker R. A transiently chaotic neural network approach to the design of cellular manufacturing. International Journal of Production Research 40 10 (2002) 2225-2244
35. Srinivasan G., and Narendran T.T. GRAFICS-A nonhierarchical clustering-algorithm for group technology. International Journal of Production Research 29 3 (1991) 463-478
36. Stanfel L.E. Machine clustering for economic production. Engineering Costs and Production Economics 9 (1985) 73-81
37. Uddin M.K., and Shanker K. Grouping of parts and machines in presence of alternative process routes by genetic algorithm. International Journal of Production Economics 76 3 (2002) 219-228
38. Venugopal V., and Narendran T.T. A genetic algorithm approach to the machining grouping problem with multiple objectives. Computers and Industrial Engineering 22 4 (1992) 469-480
39. Waghodekar P.H., and Sahu S. Machine-component cell formation in group technology MACE. International Journal of Production Research 22 (1984) 937-948
40. Wang J. Formation of machine cells and part families in cellular manufacturing systems using a linear assignment algorithm. Automatica 39 9 (2003) 1607-1615
41. Wemmerlov U., and Hyer N.L. Cellular manufacturing in the US industry: A survey of users. International Journal of Production Research 27 9 (1989) 1511-1530
42. Wu T.H., Chang C.C., and Chung S.H. A simulated annealing algorithm for manufacturing cell formation problems. Expert Systems with Applications 34 (2008) 1609-1617
43. Zhao C., and Wu Z. A genetic algorithm for manufacturing cell formation with multiple routes and multiple objectives. International Journal of Production Research 38 2 (2000) 385-395
44. Zolfaghari S., and Liang M. An objective-guided ortho-synapse Hopfield network approach to machine grouping problems. International Journal of Production Research 35 10 (1997) 2773-2792