Clustering Algorithm In Order Research Articles

Performance of Selected Part-Machine Grouping Techniques for Data Sets of Wide Ranging Sizes and Imperfection

This study addresses the part-machine grouping problem in group technology, and evaluates die performance of several cell formation methods for a wide range of data set sizes. Algorithms belonging to four classes are evaluated: (1) array-based methods: bond energy algorithm (BEA), direct clustering analysis (DCA) and improved rank order clustering algorithm (ROC2); (2) non-hierarchical clustering method: ZODIAC; (3) augmented machine matrix methods: augmented p-median method (APM) and augmented linear clustering algorithm (ALC); and (4) neural network algorithms: ART1 and variants: ART1/KS, ART1/KSC, and Fuzzy ART. The experimental design is based on a mixture-model approach, utilizing replicated clustering. The performance measures include Rand Index and bond energy recovery ratio, as well as computational requirements for various algorithms. Experimental factors include problem size, degree of data imperfection, and algorithm tested. The results show that, among the algorithms applicable for large, industry-size data sets, ALC and neural networks are superior to ZODIAC, which in turn is generally superior to array-based methods of ROC2 and DCA.

Revisiting the cell formation problem: assigning parts to production systems

Abstract Before manufacturing cells can be identified from a population of parts and machines, the parts must be carefully analysed to determine the small subset of parts that are appropriate for cellular manufacturing. When this is not done it becomes very difficult to identify the cells because the algorithms that are used have high computational requirements and high space requirements. The computational requirements and space requirements for the rank order clustering algorithm and the similarity coefficient algorithm are determined. Other algorithms that have been proposed for solving the ‘cell formation problem’ are more elaborate than these and are therefore likely have higher computational and space requirements. A procedure for assigning parts to production systems is presented in this paper. An illustrative example, taken from an actual implementation, is described to show how the procedure can be used.

Concurrent formation of part families and machine cells based on the fuzzy set theory

Abstract The goal of this research is to develop a group technology (GT) cell formation methodology, with the inclusion of fuzziness usually found in industry. Three studies are conducted. The first study establishes a procedure through which a nonbinary machine-component matrix is constructed. This matrix forms the basis of the following two studies. The second study applies the nonbinary matrix concept to the single linkage clustering algorithm. The third study focuses on a modified rank order clustering algorithm that involves fuzzy set theory. The modified single linkage and modified rank order clustering algorithm are compared and the strengths and weaknesses are discussed. The flexibility provided by the fuzzy set-based method is presented.

A close neighbour algorithm for designing cellular manufacturing systems

The first step in creating a cellular manufacturing system is to identify machine groups and form part families. Clustering and data organization (CDR) algorithms (such as the bond energy algorithm) and array sorting (ARS) methods (such as the rank order clustering algorithm) have been proposed to solve the machine and part grouping problem. However, these methods do not always produce a solution matrix that has a block diagonal structure, making visual identification of machine groups and part families extremely difficult. This paper presents a ‘close neighbour algorithm’ to solve this problem. The algorithm overcomes many deficiencies of the CDR and ASM methods. The algorithm is tested against ten existing algorithms in solving test problems from the literature. Test results show that the algorithm is very reliable and efficient.

MODROC: an extension of rank order clustering for group technology

This paper is an extension of the well known rank order clustering algorithm for group technology problems. The ROC method is analysed and its main drawbacks are identified. The present method uses the ROC algorithm in conjunction with a block and slice method for obtaining a set of intersecting machine cells and non-intersecting part families. Then a hierarchical clustering method is applied based on a measure of association among pairs of machine cells. Clustering is terminated when all the surviving cells are non-intersecting or when a single group is formed. In the latter case, the number of cells is determined on the basis of a suitable decision criterion and the bottleneck machines are identified at the appropriate hierarchical level in the clustering process.

Machine-component grouping in production flow analysis: an approach using a rank order clustering algorithm

Existing cluster analysis methods are reviewed and a new approach using a rank order clustering algorithm is described which is particularly relevant to the problem of machine-component group formation. A relaxation and regrouping procedure is developed whereby the basic rank order clustering method may be extended to the case where there are bottleneck machines.