Abstract
This paper is presented with an algorithm for manufacturing cell system design and part family identification. The model is suitable for establishing a good division of machine cells and part families considering operation sequence data. The aim of this model is the maximization of group technology efficiency value which is mostly used for measuring the worth of cellular configurations when route matrix data is considered in design. Allocating machines to different machine cells is carried out using a randomized procedure based on genetic algorithm. Five situations based on four problems were subjected to comparison based on Group Technology Efficiency (GTE) with two other methods from the literature and it is observed that the new algorithm is either outperforming the other methods or giving the best results obtained from them.
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References
Wemmerlo, V.U., Johnson, D.J.: Cellular manufacturing at 46 user plants: implementation experiences and performance improvements. Int. J. Prod. Res. 1(35), 29–49 (1997)
Pillai, V.M., Subbarao, K.A.: Robust cellular manufacturing system design for dynamic part population using a genetic algorithm. Int. J. Prod. Res. 46(1), 5191–5210 (2008)
Adenso-Diaz, B., Lozano, S.: A model for the design of dedicated manufacturing cells. Int. J. Prod. Res. 46, 301–319 (2008)
Chen, C.L., Cotruvo, N.A., Baek, W.: A simulated annealing solution to the cell formation problem. Int. J. Prod. Res. 33, 2601–2614 (1995)
Nair, J.G., Narendran, T.T.: CASE: A clustering algorithm for cell formationwith sequence data. Int. J. Prod. Res. 36, 157–179 (1998)
Park, S., Suresh, N.C.: Performance of Fuzzy ART neural network and hierarchical clustering for part machine grouping based on operation sequences. Int. J. Prod. Res. vv. 41(14), 3185–3216 (2003)
Won, Y., Lee, K.C.: Group technology cell formation considering operation sequences and production volumes. Int. J. Prod. Res. 39, 2755–2768 (2001)
Shiyas, C.R., Madhusudanan, Pillai V.: An algorithm for intra-cell machine sequence identification for manufacturing cells. Int. J. Prod. Res. 5, 2427–2433 (2014)
Alijuneidi, T., Bulgak, A.: A: designing a cellular manufacturing system featuring remanufacturing, recycling, and disposal options: a mathematical modeling approach. CIRP J. Manufact. Sci. Technol. 19, 25–35 (2017)
Kumar, C.S., Chandrasekharan, M.P.: Grouping efficacy: a quantitative criterion for goodness of block diagonal forms of binary matrices in group technology. Int. J. Prod. Res. 28, 233–243 (1990)
Harhalakis, G., Nagi, R., Proth, J.M.: An efficient heuristic in manufacturingcell formation for group technology applications. Int. J. Prod. Res. 28, 185–198 (1990)
SudhakaraPandian, R., Mahapatra, S.S.: Manufacturing cell formation with production data using neural networks. Comput. Ind. Eng. 56, 1340–1347 (2009)
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Shiyas, C.R., Radhika, B., Vineetha, G.R. (2019). A Sequence-Based Cellular Manufacturing System Design Using Genetic Algorithm. In: Mallick, P., Balas, V., Bhoi, A., Zobaa, A. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-13-0617-4_35
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DOI: https://doi.org/10.1007/978-981-13-0617-4_35
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