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Manufacturing Systems Management by Prof. G. Srinivasan, Department of Management Studies, IIT Madras. 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. Existing cluster analysis methods are reviewed and a new approach using a rank order clustering algorithm is described which is particularly.


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Production flow analysis - Wikipedia

Reapply the algorithm to generate a diagonal structure. Duplicate the bottleneck machines into each group. The case of exceptional elements Exceptional elements may disrupt the clustering process and result in a poor formation.


Identify the exceptional elements. Temporarily remove the exceptional elements from the matrix.

Production flow analysis

Reapply the algorithm to the matrix. Restore the previously ignored exceptional elements.

Measures of performance The performance of clustering can be evaluated either according to computational efficiency or according to clustering effectiveness Cunningham rank order clustering method OgilvieKuiper and FisherRand Clustering efficiency is normally measured rank order clustering method terms of program execution time, the amount of memory needed, and the complexity of the algorithm Kusiak and Chow In this study, four effective measures were selected since they have been widely used in the literature Chu Total bond energy Total bond energy BEalso called measure of effectiveness MEis an index used to measure the relative clumsiness of a clustered matrix.

Bond energy is defined as the product of the two nearest-neighbor elements McCormick et al.


The total bond energy is equal to: Theoretically, a matrix with denser clumps of elements will have a larger total bond energy; rank order clustering method, the larger BE that an algorithm can create, the better the clustering result obtained.

However, in some cases, even if the BE is not the highest, the clustering results may still be acceptable.

Per cent of exceptional elements PEobtained from dividing the number of exceptional elements by the total number of elements with '1' in the entry, is used to include the possible effect rank order clustering method problem size. A better clustering algorithms results in a smaller percentage of exceptional elements PE.

Machine utilization Machine utilization Rank order clustering method indicates the percentage of times the machines within the clusters are used in production. MU can be computed as Chandrasekharan and Rajagopalan Generally speaking, the higher the value, the better the machines are being utilized.

However, in some special cases, even if the clustering method produces a smaller percentage of exceptional elements and a higher total bond energy, the machine utilization may be lower, Grouping efficiency Grouping efficiency GE is an aggregate measure which takes both the number of exceptional elements and machine utilization into consideration.

A weight, p, must be rank order clustering method to reveal the relative importance of each term, though a value of is commonly used.

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Rank order clustering method can be defined as Chandrasekharan and Rajagopalan As a general rule, the higher the grouping efficiency, the better the clustering results. Modified Rank Order Clustering[ edit ] Modified Rank Order Clustering MROC is a weight based rank order clustering approach which was proposed into facilitate the needs of the real-world manufacturing environment.