Workforce Assignment into Large-Sized Virtual Cells Using Learning Vector Quantization (LVQ) Approach

Abstract:

Much has been reported in literature on virtual cell formation problems while a limited work is reported on worker assignments. Virtual Cellular Manufacturing Systems (VCMS) have come into existence, replacing traditional Cellular Manufacturing Systems (CMS), to meet highly dynamic production conditions in terms of demand, production lots, processing times, product mix and production sequences. Although the problem of worker assignment and flexibility in cell based manufacturing environments has been studied and analyzed in plenty using various heuristics/mathematical models, application of Artificial Neural Networks (ANN), adapted from the biological neural networks, is the recent development in this field exploiting the ability of ANN to work out mathematically-difficult-to-solve problems. In this attempt, the previous work of the author has been further developed and an attempt has been made to apply Learning Vector Quantization (LVQ) approach into worker assignment problems for higher order virtual cells i.e., three cells configurations and analyze the suitability of LVQ approach in terms of successful classification rate and simulation parameters for a number of VCMS periods.

• Periodical: Advanced Materials Research (Volume 576)
• Pages: 705-709
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.576.705
• Online since: October 2012
• Authors: R.V. Murali
• Keywords: Artificial Neural Network (ANN), Learning Vector Quantization (LVQ), Virtual Cellular Manufacturing, Worker Assignment

References

[1] Slomp, J., Chowdary, B.V. and Suresh, N.C. (2005) 'Design of virtual manufacturing cells: a mathematical programming approach', Robotics and Computer-Integrated Manufacturing, Vol. 21, p.273–288

DOI: 10.1016/j.rcim.2004.11.001

[2] Nomden, G., Slomp, J. and Suresh, N.C. (2006) 'Virtual manufacturing cells: a taxonomy of past research and identification of future research', International Journal of Flexible Manufacturing Systems, Vol. 17, p.71–92.

DOI: 10.1007/s10696-006-8122-1

[3] Bopaya Bidanda, Poonsiri Ariyawongrat, Kim LaScola Needy, Bryan A. Norman & Wipawee Tharmmaphornphilas. (2005) 'Human related issues in manufacturing cell design, implementation, and operation: a review and survey', Computers & Industrial Engineering, Vol. 48, p.507–523.

DOI: 10.1016/j.cie.2003.03.002

[4] Kaparthi, S. and Suresh, N.C. (1992) 'Machine-component cell formation in group technology: a neural network approach', International Journal of Production Research, Vol. 30(6), pp.1353-1368.

DOI: 10.1080/00207549208942961

[5] Dagli, C. and Huggahalli, R., A neural network approach to Group Technology, in J. Wang & Y. Takefugi Ed.), Neural Networks in Design and Manufacturing, (River Edge, NJ: World Scientific, 1993) 1-55.

DOI: 10.1142/9789814350969_0001

[6] Murali, R.V., A B Puri, and G.Prabhakaran, (2010), Artificial neural networks based predictive model for worker assignment into virtual cells, International Journal of Engineering, Science and Technology, Vol. 2, No. 1, 2010, pp.163-174.

DOI: 10.4314/ijest.v2i1.59108

[7] Murali, R.V., A B Puri, and G.Prabhakaran, GA-Driven ANN Model for Worker Assignment into Virtual Manufacturing Cells, Proceedings of the World Congress on Engineering 2010 Vol. III, WCE 2010, June 30 - July 2, 2010, London, U.K.

[8] James A. Freeman and David M Skapura, 'Neural Networks - Algorithms, Applications and Programming Techniques', International Student Edition, Addison Wesley, 1991,ISBN: 0-201-524449.

[9] S. Haykin, 'Neural Networks: A Comprehensive Foundation', Prentice-Hall, Englewood Cliffs, NJ, USA, 2nd edition, 1999.

[10] MATLAB, The Language of Technical Computing, Neural Network Toolbox, The Mathworks Inc., 2007.