CF-GGA: A grouping genetic algorithm for the cell formation problem

CF-GGA: A grouping genetic algorithm for the cell formation problem

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Article ID: iaor20022217
Country: United Kingdom
Volume: 39
Issue: 16
Start Page Number: 3651
End Page Number: 3669
Publication Date: Jan 2001
Journal: International Journal of Production Research
Authors: ,
Keywords: genetic algorithms, cell formation
Abstract:

In manufacturing, the machine-part cell formation (MPCF) problem addresses the issues surrounding the formation of part families based on the processing requirements of the components, and the identification of machine groups based on their ability to process specific part families. Past research has shown that one key aspect of attaining efficient groupings of parts and machines is the block-diagonalization of the given machine-part (MP) incidence matrix. This paper presents and tests a grouping genetic algorithm (GGA) for solving the MPCF problem and gauges the quality of the GGA's solutions using the measurements of efficiency and efficacy. The GGA in this study, CF-GGA, a grouping genetic algorithm for the cell formation problem, performs very well when applied to a variety of problems from the literature. With a minimal number of parameters and a straightforward encoding, CF-GGA is able to match solutions with several highly complex algorithms and heuristics that were previously employed to solve these problems.

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