×

A multi-objectice genetic algorithm approach to the design of cellular manufacturing systems. (English) Zbl 1060.90084

Summary: In this paper, a multi-objective integer programming model is constructed for the design of cellular manufacturing systems with independent cells. A genetic algorithm with multiple fitness functions is proposed to solve the formulated problem. The proposed algorithm finds multiple solutions along the Pareto optimal frontier. There are some features that make the proposed algorithm different from other algorithms used in the design of cellular manufacturing systems. These include: (1) a systematic uniform design-based technique, used to determine the search directions, and (2) searching the solution space in multiple directions instead of single direction. Four problems are selected from the literature to evaluate the performance of the proposed approach. The results validate the effectiveness of the proposed method in designing the manufacturing cells.

MSC:

90C59 Approximation methods and heuristics in mathematical programming
90C10 Integer programming
90B50 Management decision making, including multiple objectives
Full Text: DOI

References:

[1] Aktürk MS, International Journal of Production Research 34 (8) pp 2299– (1996) · Zbl 0930.90017 · doi:10.1080/00207549608905026
[2] Aktürk MS, International Journal of Production Research 38 (10) pp 2327– (2000) · Zbl 1012.90507 · doi:10.1080/00207540050028124
[3] Al-Sultan KS, Production Planning and Control 8 (8) pp 788– (1997) · doi:10.1080/095372897234687
[4] Ballakur A, International Journal of Production Research 25 (5) pp 639– (1987) · doi:10.1080/00207548708919868
[5] Choobineh F, International Journal of Production Research 26 (7) pp 1161– (1988) · doi:10.1080/00207548808947932
[6] Coello CAC, Knowledge and Information Systems 1 (3) pp 269– (1999) · doi:10.1007/BF03325101
[7] Dimopoulos C, IEEE Transactions on Evolutionary Computation 4 (2) pp 93– (2000) · doi:10.1109/4235.850651
[8] Fonseca CM, Journal of Evolutionary Computation 3 (1) pp 1– (1995) · doi:10.1162/evco.1995.3.1.1
[9] Gen M, Genetic Algorithms and Engineering Optimization pp pp. 390–450– (2000)
[10] Goldberg DE, Genetic Algorithms in Search, Optimization, and Machine Learning (1989)
[11] Gravel M, European Journal of Operational Research 109 pp 286– (1998) · Zbl 0937.90024 · doi:10.1016/S0377-2217(98)00057-5
[12] Gupta Y, International Journal of Computer Integrated Manufacturing 8 (2) pp 92– (1995) · doi:10.1080/09511929508944633
[13] Gupta Y, International Journal of Production Research 34 (2) pp 447– (1996) · Zbl 0924.90081 · doi:10.1080/00207549608904913
[14] Hajela P, Structural Optimization 4 pp 99– (1992) · doi:10.1007/BF01759923
[15] Heragu SS, IEEE Transactions on Systems, Man and Cybernetics 24 (2) pp 203– (1994) · doi:10.1109/21.281420
[16] Horn J Nafpliotis N 1993 Multi-objective optimization using the niched Pareto genetic algorithm. IlliGAL technical report 93005, Illinois Genetic Algorithms Laboratory, University of Illinois, Urbana, Illinois
[17] Kusiak A, Intelligent Manufacturing Systems (1990)
[18] Lee-Post A, International Journal of Production Research 38 (4) pp 793– (2000) · Zbl 0962.90504 · doi:10.1080/002075400189158
[19] Leung YW, IEEE Transactions on Systems, Man and Cybernetics–Part C: Applications and Reviews 30 (3) pp 293– (2000) · doi:10.1109/5326.885111
[20] Mansouri SA, International Journal of Production Research 38 (5) pp 1201– (2000) · Zbl 0945.90529 · doi:10.1080/002075400189095
[21] McAuley J, Production Engineer 51 (2) pp 53– (1972) · doi:10.1049/tpe.1972.0006
[22] Moon C, Computers & Industrial Engineering 36 pp 379– (1999) · doi:10.1016/S0360-8352(99)00138-2
[23] Plaquin M-F, International Journal of Production Economics 64 pp 267– (2000) · doi:10.1016/S0925-5273(99)00064-X
[24] Schaffer JD, Proceedings of the International Conference on Genetic Algorithms and their Applications pp pp. 93–100– (1985)
[25] Selim HM, Computers & Industrial Engineering 34 (1) pp 3– (1998) · doi:10.1016/S0360-8352(97)00147-2
[26] Shankar R, International Journal of Production Economics 55 (1) pp 97– (1998) · doi:10.1016/S0925-5273(98)00043-7
[27] Shankar R, International Journal of Production Research 37 (11) pp 2545– (1999) · Zbl 0949.90566 · doi:10.1080/002075499190653
[28] Singh N, European Journal of Operational Research 69 (3) pp 284– (1993) · doi:10.1016/0377-2217(93)90016-G
[29] Singh N, Cellular Manufacturing Systems Design, Planning and Control (1996) · doi:10.1007/978-1-4613-1187-4
[30] Soleymanpour M, International Journal of Production Research 40 (10) pp 2225– (2002) · Zbl 1051.90513 · doi:10.1080/00207540210122284
[31] Srinivas N, Journal of Evolutionary Computation 2 (3) pp 221– (1994) · doi:10.1162/evco.1994.2.3.221
[32] Su C-T, International Journal of Production Research 36 pp 2185– (1998) · Zbl 0940.90527 · doi:10.1080/002075498192841
[33] Uddin MK, International Journal of Production Economics 76 pp 219– (2002) · doi:10.1016/S0925-5273(01)00164-5
[34] Venugopal V, Computers & Industrial Engineering 22 (4) pp 469– (1992) · doi:10.1016/0360-8352(92)90022-C
[35] Wemmerlöv U, International Journal of Production Research 27 (9) pp 1511– (1989) · doi:10.1080/00207548908942637
[36] Zhao C, International Journal of Production Research 38 (2) pp 385– (2000) · Zbl 0944.90508 · doi:10.1080/002075400189473
[37] Zitzler E, Journal of Evolutionary Computation 8 (2) pp 173– (2000) · doi:10.1162/106365600568202
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.