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Domain‐concept association rules mining for large‐scale and complex cellular manufacturing tasks

Wannapa Kay Mahamaneerat (Computer Science Department, University of Missouri, Columbia, Missouri, USA)
Chi‐Ren Shyu (Computer Science Department, University of Missouri, Columbia, Missouri, USA)
Shih‐Chun Ho (Industrial and Manufacturing Systems Engineering Department, University of Missouri, Columbia, Missouri, USA)
C. Alec Chang (Industrial and Manufacturing Systems Engineering Department, University of Missouri, Columbia, Missouri, USA)

Journal of Manufacturing Technology Management

ISSN: 1741-038X

Article publication date: 11 September 2007

660

Abstract

Purpose

The purpose of this paper is to provide a novel domain‐concept association rules (DCAR) mining algorithm that offers solutions to complex cell formation problems, which consist of a non‐binary machine‐component (MC) matrix and production factors for fast and accurate decision support.

Design/methodology/approach

The DCAR algorithm first identifies the domain‐concept from the demand history and then performs association rule mining to find associations among machines. After that, the algorithm forms machine‐cells with a series of inclusion and exclusion processes to minimize inter‐cell material movement and intra‐cell 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 machine‐cells with production costs when extra parameters are needed.

Research limitations/implications

The DCAR algorithm adapts the idea of the sequential forward floating selection (SFFS) to iteratively evaluate and arrange machine‐cells until the result is stabilized. The SFFS is an improvement over a greedy version of the algorithm, but can only ensure sub‐optimal solutions. Practical implications – The DCAR algorithm considers a wide range of production parameters, which make the algorithm suitable to the real‐world manufacturing system settings.

Originality/value

The proposed DCAR algorithm is unlike other array‐based algorithms. It can group non‐binary MC matrix with considerations of real‐world factors including product demand, BOM, costs, and maximum number of machines allowed for each cell.

Keywords

Citation

Kay Mahamaneerat, W., Shyu, C., Ho, S. and Alec Chang, C. (2007), "Domain‐concept association rules mining for large‐scale and complex cellular manufacturing tasks", Journal of Manufacturing Technology Management, Vol. 18 No. 7, pp. 787-806. https://doi.org/10.1108/17410380710817255

Publisher

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Emerald Group Publishing Limited

Copyright © 2007, Emerald Group Publishing Limited

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