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2012 (Vol. 4, Issue: 15)
Article Information:

Workforce Assignment into Virtual Cells using Learning Vector Quantization (LVQ) Approach

R.V. Murali
Corresponding Author:  R.V. Murali 

Key words:  Artificial neural networks, Learning Vector Quantization (LVQ), virtual cellular manufacturing, worker assignment, , ,
Vol. 4 , (15): 2427-2435
Submitted Accepted Published
February 13, 2012 March 15, 2012 August 01, 2012

In this study, an attempt has been made to apply Learning Vector Quantization (LVQ) approach, one of the network types of Artificial Neural Networks (ANN), into worker assignment problems for VCMS environment and analyze the network performance and effectiveness under different cell configurations and time periods. Worker assignment problems assume a crucial role in any type of manufacturing systems due to the fact that it is one of the major resource implicating factors. Its influence is much more significant in case of a dynamic production environment such as cell-based manufacturing systems. In this type production environment, product variety is changing very rapidly prompting the need to redesign the production facility quickly so as to accommodate agility. 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. Traditional CMS involves formation of machine cells and part families based on the similarity characteristics in the product and process route. While cell formation phase has been dealt quite voluminously, researchers have started realizing, not long before, that workers’ role during implementation of this cell-based manufacturing systems has been a major dimension. The problem of worker assignment and flexibility in cell based manufacturing environments has been studied and analyzed in plenty and various heuristics/mathematical models are developed to achieve reduced labor costs, improved productivity and quality, effective utilization of workforce and providing adequate levels of labor flexibility. Application of ANN, adapted from the biological neural networks, is the recent development in this field exploiting its ability to work out mathematically-difficult-to-solve problems. Previous studies of the author have prompted that ANN technique is a useful approach for solving worker assignment problems while the present study expands the previous efforts through applying a unique class of ANN i.e., LVQ into worker assignment problems for VCMS environment. The results obtained in this study affirm that LVQ based approach is useful and effective under different cell configurations and time periods.
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  Cite this Reference:
R.V. Murali, 2012. Workforce Assignment into Virtual Cells using Learning Vector Quantization (LVQ) Approach.  Research Journal of Applied Sciences, Engineering and Technology, 4(15): 2427-2435.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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