The modified fuzzy art and a two-stage clustering approach to cell design

Özdemir, Rıfat Gürcan; Gençyılmaz, Güneş; Aktin, Tülin

doi:10.1016/j.ins.2007.06.027

Cited by 19 publications

(9 citation statements)

References 27 publications

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“…Adaptive neural networks always had an important role in CM beginning in the early 1990s in the works of Kao and Moon (1991), Malave and Ramachandran (1991), Dagli and Huggahalli (1991) and Moon and Chi (1992). Fuzzy ART was another common adaptive resonance framework as presented in the works of Suresh and Kaparthi (1994), Burke and Kamal (1995), Kamal and Burke (1996), Suresh et al (1999), Peker and Kara (2004), Won and Currie (2007) and Ozdemir et al (2007) that provided a unified architecture for both binary and continuous valued inputs. Fuzzy ART was another common adaptive resonance framework as presented in the works of Suresh and Kaparthi (1994), Burke and Kamal (1995), Kamal and Burke (1996), Suresh et al (1999), Peker and Kara (2004), Won and Currie (2007) and Ozdemir et al (2007) that provided a unified architecture for both binary and continuous valued inputs.…”

Section: Fuzzy Adaptive Resonance Theorymentioning

confidence: 99%

Fuzzy ART K-Means Clustering Technique: a hybrid neural network approach to cellularmanufacturing systems

Sengupta

Ghosh

Dan

2011

International Journal of Computer Integrated Manufacturing

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Cellular manufacturing system (CMS) is regarded as an efficient production strategy for batch type of production. Literature suggests, since the last two decades neural network has been intensively used in cell formation while production factor such as operation time is merely considered. This paper presents a new hybrid neural network approach, Fuzzy ART K-Means Clustering Technique (FAKMCT), to solve the part machine grouping problem in CMS considering operation time. The performance of the proposed technique is tested with problems from open literature and the results are compared to the existing clustering models such as simple K-means algorithm and modified ART1 algorithm as found in the recent literature. The results support the better performance of the proposed algorithm. The novelty of this study lies in the simple and efficient methodology to produce quick solutions with least computational efforts.

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Section: Fuzzy Adaptive Resonance Theorymentioning

confidence: 99%

Fuzzy ART K-Means Clustering Technique: a hybrid neural network approach to cellularmanufacturing systems

Sengupta

Ghosh

Dan

2011

International Journal of Computer Integrated Manufacturing

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“…Recent advances incorporate practical consideration such as dynamic cell configuration, alternative routing, sequence of operation, capacity, workload, and setup times [32]. In addition, research in this area have applied advanced tools to classify parts using simulated annealing hybrid algorithms [33] and fuzzy ART neural networks for machine-part clustering based on sequence data [34][35][36][37]. The latter is discussed in further detail in the next section.…”

Section: Group Technology and Cellular Manufacturingmentioning

confidence: 99%

Team Formation in Concurrent Engineering Using Group Technology (GT) Concepts

Aleisa

Lin

2011

Concurrent Engineering

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Concurrent engineering (CE) has emerged as an essential design principle that facilitates rapid and efficient product development that is necessary to survive in today’s fiercely competitive environment. However, the lack of tools that address design team formulation and proper design task assignment in CE has made its application an immense challenge. In this article we explicitly address this issue by pointing out the relevance of its structure to cell-formation problem in cellular manufacturing (CM) and group technology. Particularly, interdisciplinary CE teams will be formulated as multifunctional machine-cells that process similar parts, in our case, design tasks requiring involvement of similar individuals. The fruitful marriage of the two disciplines, concurrent engineering and cellular manufacturing, opens the path towards utilizing the ample existing efficient algorithms of cellular manufacturing to concurrent engineering rather than re-inventing the wheel in developing methods in this respect. The paper in-hand also incorporate sequences (or routing in cellular manufacturing) to create work teams that can conduct similar tasks commonly known as work packages in a smoother manner that avoids unnecessary back and forth alterations and overlapping. In addition, to enable solving the task assignment problem in reasonable time for relatively larger problems, the developed framework integrates a fuzzy ART algorithm proven efficient in cellular manufacturing literature. The article also provides an illustrative example on executing the methodology on a design example and highlights its implications on task team assignments.

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“…Dagli and Huggahalli used ART1 in such problems while Malave and Ramachandran used competitive learning. Fuzzy ART was another common adaptive resonance framework as presented in the works of Suresh and Kaparthi (1994), Burke and Kamal (1995), Kamal and Burke (1996), Suresh et al (1999), Peker and Kara (2004), Won and Currie (2007) and Ozdemir et al (2007) which provided a unified architecture for both binary and continuous valued inputs. Although fuzzy ART does not require a completely binary representation of the parts to be grouped, it possesses the same desirable stability properties as ART1 and a simpler architecture than that of ART2.…”

Section: Fuzzy Adaptive Resonance Theorymentioning

confidence: 99%

A hybrid neural network approach to cell formation in cellular manufacturing

Sengupta

Ghosh

Dan

2011

IJISTA

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The design of Cellular Manufacturing Systems (CMS) has attained the significant interest of academicians, researchers and practitioners over the last three decades. CMS is regarded as an efficient production strategy for batch type of production. Literature suggests that since the last two decades neural network based methods have been intensively used in cell formation problems while production factor such as operation time is merely considered. This paper presents a new hybrid neural network approach, Fuzzy ART-Centroid Linkage Clustering Technique (FACLCT), to solve the part machine grouping problems in cellular manufacturing systems considering operation time. The performance of the proposed technique is tested with problems from open literature and the results are compared with the existing clustering models such as simple C-Linkage, K-Means, modified ART1 and genetic algorithm and achieved better performance. The novelty of this study lies in the simple and efficient methodology to produce quick solutions with least computational efforts.

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The modified fuzzy art and a two-stage clustering approach to cell design

Cited by 19 publications

References 27 publications

Fuzzy ART K-Means Clustering Technique: a hybrid neural network approach to cellularmanufacturing systems

Fuzzy ART K-Means Clustering Technique: a hybrid neural network approach to cellularmanufacturing systems

Team Formation in Concurrent Engineering Using Group Technology (GT) Concepts

A hybrid neural network approach to cell formation in cellular manufacturing

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