Pattern Classification Based on Part–Tool Matrix Elements

Finally, neural networks are beginning to be utilized in other, related grouping applications as well.

Arizono et al. [1995] presented a stochastic neural network to solve the part–tool grouping problem for flexible manufacturing systems (FMS). Deterministic neural network models do not have the capability to escape from local optimal solution. Stochastic neural network models attempt to avoid local optimal solutions. Stochastic neural network models include the Boltzmann machine model and Gaussian machine model. Arizono et al. [1995] formulated the part–tool grouping problem as a 0–1 integer programming problem, and utilized a recurrent network, stochastic network along the lines of the Boltzmann machine.

4.4 Conclusions

In summary, artificial networks have emerged as a useful addition to the set of tools and techniques for the application of group technology and design of cellular manufacturing systems. The application of neural networks for the part–machine grouping problem, in particular, have produced very encouraging results. Neural networks also hold considerable promise, in general, for pattern classification and com-plexity reduction in logistics, and for streamlining and synergistic regrouping of many operations in the supply chain.

This chapter provided a taxonomy of the literature, tracing the evolution of neural network applications for GT/CM. A concise summary of the workings of several supervised and unsupervised networks for this application domain was provided along with a summary of their implementation requirements, pros and cons, computational performance, and domain of applicability. Along with the contents of this chapter, and the references cited, the interested reader is referred to Zhang and Huang [1995] for a review of artificial neural networks for other, closely related areas in manufacturing.

References

Arizono, I., Kato, M., Yamamoto, A. and Ohta, H., 1996, A new stochastic neural network model and its application to grouping parts and tools in flexible manufacturing systems, Int. J. Prod. Res., 33(6):

1535-1548.

Burbidge, J.L., 1963, Production flow analysis, Prod. Eng., 42:742.

Burbidge, J.L., 1989, Production Flow Analysis, Clarendon Press, Oxford, UK.

Burke, L.I., 1991, Introduction to artificial neural systems for pattern recognition, Comp. Ops. Res. 18(2):

211-220.

Burke, L.I. and Kamal, S., 1992, Fuzzy ART and cellular manufacturing, ANNIE 92 Conf. Proc., 779-784.

Burke, L.I. and Kamal, S., 1995, Neural networks and the part family/machine group formation problem in cellular manufacturing: A framework using Fuzzy ART, J. Manuf. Sys., 14(3): 148-159.

24(2): 189-198.

Chen, S.-J. and Cheng, C.-S., 1995, A neural network-based cell formation algorithm in cellular manu-facturing, Int. J. Prod. Res., 33(2): 293-318.

Chu, C.H., 1989, Clustering analysis in manufacturing cell formation, OMEGA: Int. J. Mgt. Sci., 17: 289-295.

Chu, C.H., 1993, Manufacturing cell formation by competitive learning, Int. J. Prod. Res., 31(4): 829-843.

Chung, Y. and Kusiak, A., 1994, Grouping parts with a neural network, J. Manuf. Systems, 13(4): 262-275.

Coyne, R.D. and Postmus, A.G., 1990, Spatial application of neural networks in computer aided design, Artificial Intelligence in Engineering, 5(1), 9-22.

Currie, K.R., 1992, An intelligent grouping algorithm for cellular manufacturing, Comp. Ind. Eng., 23(1):

109-112.

Dagli, C. and Huggahalli, R., 1991, Neural network approach to group technology, in Knowledge-Based Systems and Neural Networks, Elsevier, New York, 213-228.

Dagli, C. and Huggahalli, R., 1995, Machine-part family formation with the adaptive resonance theory paradigm, Int. J. Prod. Res., 33(4): 893-913.

Dagli, C. and Sen, C.F., 1992, ART1 neural network approach to large scale group technology problems, in Robotics and Manufacturing: Recent Trends in Research, Education and Applications, 4, ASME Press, New York, 787-792.

Jamal, A.M.M., 1993, Neural network and cellular manufacturing, Ind. Mgt. Data Sys., 93(3): 21-25.

Kamal, S. and Burke, L.I., 1996, FACT: A new neural network based clustering algorithm for group technology, Int. J. Prod. Res., 34(4): 919-946.

Kamarthi, S.V., Kumara, S.T., Yu, F.T.S. and Ham, I., 1990, Neural networks and their applications in component design data retrieval, J. Intell. Manuf., 1(2), 125-140.

Kao, Y. and Moon, Y.B., 1991, A unified group technology implementation using the back-propagation learning rule of neural networks, Comp. Ind. Eng., 20(4): 425-437.

Kaparthi, S. and Suresh, N.C., 1991, A neural network system for shape-based classification and coding of rotational parts, Int. J. Prod. Res., 29(9): 1771-1784.

Kaparthi, S. and Suresh, N.C., 1992, Machine-component cell formation in group technology: Neural network approach, Int. J. Prod. Res., 30(6): 1353-1368.

Kaparthi, S., Suresh, N.C. and Cerveny, R., 1993, An improved neural network leader algorithm for part–machine grouping in group technology, Eur. J. Op. Res., 69(3): 342-356.

Kaparthi, S. and Suresh, N.C., 1994, Performance of selected part-machine grouping techniques for data sets of wide ranging sizes and imperfection, Decision Sci., 25(4): 515-539.

Kiang, M.Y., Hamu, D. and Tam, K.Y., 1992, Self-organizing map networks as a clustering tool — An application to group technology, in V.C. Storey and A.B. Whinston (Eds.), Proc. Second Annual Workshop on Information Technologies and Systems, Dallas, TX, 35-44.

Kiang, M.Y., Kulkarni, U.R. and Tam, K.Y., 1995, Self-organizing map network as an interactive clustering tool — An application to group technology, Decision Support Sys., 15(4): 351-374.

King, J.R. and Nakornchai, V., 1982, Machine-component group formation in group technology: Review and extension, Int. J. Prod. Res., 20(2): 117.

Kohonen, T., 1984, Self-Organisation and Associative Memory, Springer-Verlag, Berlin.

Kosko, B., 1992, Neural Networks and Fuzzy Systems, Prentice-Hall International, Englewood Cliffs, NJ.

Kulkarni, U.R. and Kiang, M.Y., 1995, Dynamic grouping of parts in flexible manufacturing systems — A self-organizing neural networks approach, Eur. J. Op. Res., 84(1): 192-212.

design of cellular manufacturing systems, J. Intell. Manuf., 3: 325-332.

Liao, T.W. and Chen, J.L., 1993, An evaluation of ART1 neural models for GT part family and machine cell forming, J. Manuf. Sys., 12(4): 282-289.

Liao, T.W. and Lee, K.S., 1994, Integration of feature-based CAD system and an ART1 neural model for GT coding and part family forming, Comp. Ind. Eng., 26(1): 93-104.

Lippmann, R.P., 1987, An introduction to computing with neural nets, IEEE ASSP Mag., 4-22.

Malakooti, B. and Yang, Z., 1995, A variable-parameter unsupervised learning clustering neural network approach with application to machine-part group formation, Int. J. Prod. Res., 33(9): 2395-2413.

Malave, C.O. and Ramachandran, S., 1991, A neural network-based design of cellular manufacturing system, J. Intell. Mfg., 2: 305-314.

McClelland, J.L. and Rumelhart, D.E., 1988, Explorations in Parallel Distributed Processing: A Handbook of Models, Programs and Exercises, MIT Press, Cambridge, MA.

McCormick, W.T., Schweitzer, P.J. and White, T.W., 1972, Problem decomposition and data reorganization by a clustering technique, Op. Res., 20(5), 992-1009.

Moon, Y.B., 1990a, An interactive activation and competition model for machine-part family formation in group technology, Proc. Int. Joint Conf. on Neural Networks, Washington, D.C., vol. 2, 667-670.

Moon, Y.B., 1990b, Forming part families for cellular manufacturing: A neural network approach, Int. J.

Adv. Manuf. Tech., 5: 278-291.

Moon, Y.B., 1998, Part family identification: New pattern recognition technologies, in N.C. Suresh and J.M. Kay, (Eds.), Group Technology and Cellular Manufacturing: State-of-the-Art Synthesis of Research and Practice, Kluwer Academic Publishers, Boston.

Moon, Y.B. and Chi, S.C., 1992, Generalised part family formation using neural network techniques, J.

Manuf. Sys., 11(3): 149-159.

Moon, Y.B. and Roy, U., 1992, Learning group technology part families from solid models by parallel distributed processing, Int. J. Adv. Mfg. Tech., 7: 109-118.

Rao, H.A. and Gu, P., 1994, Expert self-organizing neural network for the design of cellular manufacturing systems, J. Manuf. Sys., 13(5): 346-358.

Rao, H.A. and Gu, P., 1995, A multi-constraint neural network for the pragmatic design of cellular manufacturing systems, Int. J. Prod. Res., 33(4): 1049-1070.

Suresh, N.C. and Kaparthi, S., 1994, Performance of Fuzzy ART neural network for group technology cell formation, Int. J. Prod. Res., 32(7): 1693-1713.

Suresh, N.C. and Kay, J.M. (Eds.), 1998, Group Technology and Cellular Manufacturing: State-of-the-Art Synthesis of Research and Practice, Kluwer Academic Publishers, Boston.

Suresh, N.C., Slomp, J. and Kaparthi, S., 1995, The capacitated cell formation problem: a new hierarchical methodology, Int. J. Prod. Res., 33(6): 1761-1784.

Suresh, N.C., Slomp, J. and Kaparthi, S., 1999, Sequence-dependent clustering of parts and machines: A Fuzzy ART neural network approach, Int. J. Prod. Res., 37(12): 2793-2816.

Venugopal, V., 1998, Artificial neural networks and fuzzy models: New tools for part-machine grouping, in N.C. Suresh, and J.M. Kay, (Eds.), Group Technology and Cellular Manufacturing: State-of-the-Art Synthesis of Research and Practice, Kluwer Academic Publishers, Boston.

Venugopal, V. and Narendran, T.T., 1992a, A neural network approach for designing cellular manufac-turing systems, Advances in Modelling and Analysis, 32(2): 13-26.

Venugopal, V. and Narendran, T.T., 1992b, Neural network model for design retrieval in manufacturing systems, Comp. Industry, 20: 11-23.

5

Application of Fuzzy Set