Abstract
This paper presents a two-stage approach consisting of a real-coded genetic algorithm and goal programming to obtain improved cell formation. In the first stage, the minimum value of each objective is determined using a single-objective genetic algorithm. In the second stage, goal programming is incorporated and the final objective is constructed as the minimization of sum of deviational variables of corresponding objectives. The proposed technique is implemented as a software toolkit using C Sharp.net programming language. Modified grouping efficiency is used as the performance measure to test the efficiency of the proposed technique. Five problems with different sizes have been considered from the literature to show the potentials of the proposed technique.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Burbidge, J. L.: The introduction of group technology. Heinemann Press, London (1975).
Dimopoulos, C., Zalzala, A. M.S.: Evolutionary Computation Approaches to Cell Optimization. Adaptive Computing in Design and Manufacture, Parmee, I. C. (Ed.), pp. 69–83. Springer-Verlag, London (1998).
Mak, K. L., Wong, Y.S.: Genetic design of cellular manufacturing systems. Human Factors and Ergonomics in Manufacturing, 10(2), 177–192 (2000).
Shanker, R., Vrat, P.: Post design modeling for cellular manufacturing system with cost uncertainty. International Journal of Production Economics, 55, 97–109 (1998).
Chi, S.C., Yan, M.C. : A fuzzy genetic algorithm for high-tech cellular manufacturing system design. IEEE Annual Meeting of the Fuzzy Information, 2, 907–912 (2004).
Gupta, Y., Gupta, M., Kumar, A., Sundaram, C.: A genetic algorithm-based approach to cell composition and layout design problems. International Journal of Production Research, 34(2), 447–482 (1996).
Pai, P.F., Chang, P.T., Lee, S.H.: Part-machine family formation using genetic algorithms in a fuzzy environment. International Journal Advanced Manufacturing Technology, 25(11–12), 1175–1179 (2005).
Mahapatra, S.S., Pandian, R.S.: Genetic cell formation using ratio level data in cellular manufacturing systems. The International Journal of Advanced Manufacturing Technology, 38(5), 630–640 (2008).
Shafer, S.M., Rogers, D.F.: A goal programming approach to the cell formation problem. Journal of Operations Management, 10(1), 28–43 (1991).
Defersha, F.M., Chen, M.: A linear programming embedded genetic algorithm for an integrated cell formation and lot sizing considering product quality. European Journal of Operational Research, 187, 46–69 (2008).
Chandrasekharan, M.P., Rajagopalan, R.: An ideal seed non-hierarchical clustering algorithm for cellular manufacturing. International Journal of Production Research, 24(2), 451–464 (1986a).
Kumar, C.S., 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, 28, 233–243 (1990).
Zolfaghari, S., Liang, M.: A new genetic algorithm for the machine/part grouping problem involving processing times and lot sizes. Computers and Industrial Engineering, 45, 713–731 (2003).
Venugopal, V., Narendran, T.T.: Cell formation in manufacturing systems through simulated annealing. European Journal of Operations Research, 63, 409–422 (1992a).
Venugopal, V., Narendran, T.T.: A Genetic algorithm approach to the machine component and grouping problem with multiple objectives. Computers and Industrial Engineering, 224, 469–480 (1992b).
Venugopal, V., Narendran, T.T.: Neural network model for design retrieval in manufacturing systems. Computers in Industry, 20, 11–23(1992c).
Srinivasan, G., Narendran, T.T.: GRAFICS: a non-hierarchical clustering algorithm for group technology. International Journal of Production Research, 29 (3), 463–478 (1991).
Kusiak, A.: The generalized group technology concept. International Journal of Production Research, 25(4), 561–569 (1987).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chaudhuri, B., Jana, R.K., Dan, P.K. (2017). A Hybrid Genetic Algorithm for Cell Formation Problems Using Operational Time. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-10-3153-3_13
Download citation
DOI: https://doi.org/10.1007/978-981-10-3153-3_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3152-6
Online ISBN: 978-981-10-3153-3
eBook Packages: EngineeringEngineering (R0)