Domainconcept association rules mining for largescale and complex cellular manufacturing tasks

Purpose The purpose of this paper is to provide a novel domainconcept association rules DCAR mining algorithm that offers solutions to complex cell formation problems, which consist of a nonbinary machinecomponent MC matrix and production factors for fast and accurate decision support. Designmethodo...

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Bibliographic Details
Published in:Journal of manufacturing technology management 2007-09, Vol.18 (7), p.787-806
Main Authors: Kay Mahamaneerat, Wannapa, Shyu, ChiRen, Ho, ShihChun, Alec Chang, C.
Format: Article
Language:English
Subjects:
Cellular Manufacturing
Data handling
Flexible manufacturing systems
Group technology
Online Access:Get full text
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Summary:Purpose The purpose of this paper is to provide a novel domainconcept association rules DCAR mining algorithm that offers solutions to complex cell formation problems, which consist of a nonbinary machinecomponent MC matrix and production factors for fast and accurate decision support. Designmethodologyapproach The DCAR algorithm first identifies the domainconcept from the demand history and then performs association rule mining to find associations among machines. After that, the algorithm forms machinecells with a series of inclusion and exclusion processes to minimize intercell material movement and intracell void element costs as well as to maximize the grouping efficacy with the constraints of bill of material BOM and the maximum number of machines allowed for each cell. Findings The DCAR algorithm delivers either comparable or better results than the existing approaches using known binary datasets. The paper demonstrates that the DCAR can obtain satisfying machinecells with production costs when extra parameters are needed. Research limitationsimplications The DCAR algorithm adapts the idea of the sequential forward floating selection SFFS to iteratively evaluate and arrange machinecells until the result is stabilized. The SFFS is an improvement over a greedy version of the algorithm, but can only ensure suboptimal solutions. Practical implications The DCAR algorithm considers a wide range of production parameters, which make the algorithm suitable to the realworld manufacturing system settings. Originalityvalue The proposed DCAR algorithm is unlike other arraybased algorithms. It can group nonbinary MC matrix with considerations of realworld factors including product demand, BOM, costs, and maximum number of machines allowed for each cell.
ISSN:1741-038X
DOI:10.1108/17410380710817255