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An Enhanced Grouping Genetic Algorithm for solving the cell formation problem

Lookup NU author(s): Teerawut Tunnukij, Professor Christian Hicks

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Abstract

Cell formation is often the first step in solving facility layout design problems. The objective is to group part families and machines so that they can be assigned to manufacturing cells. The cell formation problem is a non-deterministic polynomial (NP) complete problem which means that the time taken to produce solutions increases exponentially with problem size. This paper presents the Enhanced Grouping Genetic Algorithm (EnGGA) that has been developed for solving the cell formation problem. The EnGGA replaces the replacement heuristic in a standard Grouping Genetic Algorithm with a Greedy Heuristic and employs a rank-based roulette-elitist strategy, which is a new mechanism for creating successive generations. The EnGGA was tested using well-known data sets from the literature. The quality of the solutions was compared with those produced by other methods using the grouping efficacy measure. The results show that the EnGGA is effective and outperforms or matches the other methods.

Publication metadata

Author(s): Tunnukij T, Hicks C

Publication type: Article

Publication status: Published

Journal: International Journal of Production Research

Year: 2009

Volume: 47

Issue: 7

Pages: 1989-2007

Print publication date: 01/01/2009

Date deposited: 10/05/2010

ISSN (print): 0020-7543

ISSN (electronic): 1366-588X

Publisher: Taylor & Francis

URL: http://dx.doi.org/10.1080/00207540701673457

DOI: 10.1080/00207540701673457

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