ZODIAC—an algorithm for concurrent formation of part-families and machine-cells

Muraleedharan, C.; Rajagopalan, R.

doi:10.1080/00207548708919880

Cited by 360 publications

(88 citation statements)

References 9 publications

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“…By applying the proposed method, a solution is obtained and represented in Figure 9, where the parts' demands are shown on the last row. The grouping efficacy of this solution is 0.8403, which matches the result in Chandrasekharan and Rajagopalan [56] and other recent results [7,39]. Also, the execution time of this problem is less than 0.5 second based on Matlab implementation using a typical computer (speed: 3.1 GHz, RAM: 8 GB).…”

Section: Three Examples To Examine the Basic Functionalitysupporting

confidence: 86%

“…The third example comes from Chandrasekharan and Rajagopalan [56], which was originally a CF problem of 40 machines and 100 parts with binary information only. To examine the handling of sequence and demand information, the operation sequence is generated for each part based on the order of the machine indices (a method used by Spiliopoulos and Sofianopoulou [57]).…”

Section: Three Examples To Examine the Basic Functionalitymentioning

confidence: 99%

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Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information

Zhu

2018

Mathematical Problems in Engineering

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The purpose of this paper is to advance the similarity coefficient method to solve cell formation (CF) problems in two aspects. Firstly, while numerous similarity coefficients have been proposed to incorporate different production factors in literature, a weighted sum formulation is applied to aggregate them into a nonbinary matrix to indicate the dependency strength among machines and parts. This practice allows flexible incorporation of multiple production factors in the resolution of CF problems. Secondly, a twomode similarity coefficient is applied to simultaneously form machine groups and part families based on the classical framework of hierarchical clustering. This practice not only eliminates the sequential process of grouping machines (or parts) first and then assigning parts (or machines), but also improves the quality of solutions. The proposed clustering method has been tested through twelve literature examples. The results demonstrate that the proposed method can at least yield solutions comparable to the solutions obtained by metaheuristics. It can yield better results in some instances, as well.

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Section: Three Examples To Examine the Basic Functionalitysupporting

confidence: 86%

Section: Three Examples To Examine the Basic Functionalitymentioning

confidence: 99%

Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information

Zhu

2018

Mathematical Problems in Engineering

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“…Both the single linkage clustering algorithm and the average clustering algorithm are hierarchical clustering algorithms. The only known non-hierarchical clustering reported so far include the MacQueen's k-means method [14] which requires the number of clusters to be specified in advance, the ideal-seed algorithm [15] and its improved version ZODIAC [16].…”

Section: Machine Cell Formation Techniquesmentioning

confidence: 99%

Cell Formation in Group Technology: A Similarity Order Clustering Approach

Onwubolu

2012

The South African Journal of Industrial Engineering

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Grouping parts into families which can be produced by a cluster of machine cells is the cornerstone of cellular manufacturing, which in turn is the building block for flexible manufacturing systems. Cellular manufacturing is a group technology (GT) concept that has recently attracted the attention of manufacturing firms operating under jobshop environment to consider redesigning their manufacturing systems so as to take advantage of increased throughput, reduction in work-in-progress, set-up time, and lead times; leading to product quality and customer satisfaction. The paper presents a generalised approach for machine cell formation from a jobshop using similarity order clustering technique for preliminary cell grouping and considering machine utilisation for the design of nonintergrouping material handling using the single-pass heuristic. The work addresses the shortcomings of cellular manufacturing systems design and implementations which ignore machine utilisations, group sizes and intergroup moves.

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“…The efficiency ranges from 0 to 1. Group efficiency has been reported has a lower discriminating power (Chandrasekharan & Rajagopalan, 1987). Even an extremely bad solution with large number of exceptional elements has an efficiency value as high as 0.77.…”

Section: Grouping Efficiencymentioning

confidence: 99%

Application Similarity Coefficient Method to Cellular Manufacturing

Yin¹

2006

Manufacturing the Future

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ZODIAC—an algorithm for concurrent formation of part-families and machine-cells

Cited by 360 publications

References 9 publications

Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information

Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information

Cell Formation in Group Technology: A Similarity Order Clustering Approach

Application Similarity Coefficient Method to Cellular Manufacturing

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