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Abstract
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Article Information:
Workforce Assignment into Virtual Cells using Learning Vector Quantization (LVQ) Approach
R.V. Murali
Corresponding Author: R.V. Murali
Submitted: February 13, 2012
Accepted: March 15, 2012
Published: August 01, 2012 |
Abstract:
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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.
Key words: Artificial neural networks, Learning Vector Quantization (LVQ), virtual cellular manufacturing, worker assignment, , ,
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Cite this Reference:
R.V. Murali, . Workforce Assignment into Virtual Cells using Learning Vector Quantization (LVQ) Approach. Research Journal of Applied Sciences, Engineering and Technology, (15): 2427-2435.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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