GRAFICS—a nonhierarchical clustering algorithm for group technology

Srinivasan, G.; Narendran, T.T.

doi:10.1080/00207549108930083

Cited by 195 publications

(63 citation statements)

References 36 publications

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“…This contradicts the fundamental philosophy of GT that groups exist naturally and the task of analysis is to identify them if they exist [9,26]. Moreover, there is a limited industrial application due to unavailability of software programme supporting solution approaches of CF problem or the software is expensive [27].…”

Section: Literature Reviewmentioning

confidence: 92%

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Concurrently part-machine groups formation with important production data

Kumar¹,

Jain²

2010

Int. j. simul. model.

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This work presents an algorithm for design of cellular manufacturing system. It considers sequence of operation for every component as ordinal level data. The ratio level data includes operation time per unit, production volume and machine capacity. A matrix model is developed to incorporate ordinal level data and ratio level data. Model is then standardized. From this, Part -Part correlation model is formed. This is undergone through principle component analysis. Now, Feature-Vector and New Standardized Sequence Part Load matrices are formed. These are used for grouping purpose. Part-machine orthographic view is used for concurrent part-machine family formation. Performance of algorithm is comparable and of good quality with existing methods in its class. To facilitate industrial application, it can be implemented by using free software, Scilab and/or commercial softwares.

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Section: Literature Reviewmentioning

confidence: 92%

“…binary 0 or 1) entries of part-machine incidence matrix alone [1][2][3][4][5][6][7][8][9][10][11]. Many researchers considered similarities coefficients based approaches to solve cell formation (CF) problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Concurrently part-machine groups formation with important production data

Kumar¹,

Jain²

2010

Int. j. simul. model.

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“…But these cannot lead to satisfactory results. Some of the non-hierarchical methods are ZODIAC [2] and GRAPHICS [24].…”

Section: B Cell Formation Techniquesmentioning

confidence: 99%

Part Family and Operations Group Formation for RMS Using Bond Energy Algorithm

Khanna¹,

Kumar²

2017

IJET

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Abstract-In the present global scenario companies are forced to compete in product varieties and speed to market as well as in price. Reconfigurable Manufacturing Systems (RMSs) have been envisaged as a manufacturing paradigms to face these challenges. The foundation of RMS lies on the premise of group technology. The present work highlights the peculiarities of RMS vis a vis CMS, another group technology based manufacturing paradigm. It attempts to evolve a methodology for RMS cell formation; composition of part families and subsequently recognition of operation groups. Bond Energy Algorithm has been incorporated in the present work and considered as the foundation technique to structure the part family formation in RMS. The effectiveness of the methodology has been compared with some standard problems from the CMS cell formation literature and provides an outline for the RMS design which provides assuring manufacturing paradigm for future.

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“…Methods based upon the machine-part incidence matrix include the Bond Energy Algorithm (McCormick, et al 1972), the Direct Clustering Algorithm (Chan andMilner 1982), Rank Order Clustering (King 1980), MODROC (Chandrasekharan and Rajagopalan 1986), ZODIAC (Chandrasekharan and Rajagopalan 1987), GRAFICS (Srinivasan and Narendran 1991) and the Close Neighbour Algorithm (Boe and Cheng 1991 Methods based upon similarity coefficients have been used as an alternative approach for both part family grouping (Carrie 1973) and machine grouping (McAuley 1972, Gupta andSeifoddini 1990). A number of similarity and dissimilarity coefficients between parts and/or machines have been proposed for grouping part families and/or machine cells (Shafer and Rogers 1993a, 1993b, Islam and Sarker 2000.…”

Section: The Cell Formation Problemmentioning

confidence: 99%

An Enhanced Grouping Genetic Algorithm for solving the cell formation problem

Tunnukij

Hicks

2009

International Journal of Production Research

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International audienceCell formation is often the first step in solving facility layout design problems. The objective is to group part families and machines so that they can be assigned to manufacturing cells. The cell formation problem is a non-deterministic polynomial (NP) complete problem which means that the time taken to produce solutions increases exponentially with problem size. This paper presents the Enhanced Grouping Genetic Algorithm (EnGGA) that has been developed for solving the cell formation problem. The EnGGA replaces the replacement heuristic in a standard Grouping Genetic Algorithm with a Greedy Heuristic and employs a rank-based roulette-elitist strategy, which is a new mechanism for creating successive generations. The EnGGA was tested using well-known data sets from the literature. The quality of the solutions was compared with those produced by other methods using the grouping efficacy measure. The results show that the EnGGA is effective and outperforms or matches the other methods

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GRAFICS—a nonhierarchical clustering algorithm for group technology

Cited by 195 publications

References 36 publications

Concurrently part-machine groups formation with important production data

Concurrently part-machine groups formation with important production data

Part Family and Operations Group Formation for RMS Using Bond Energy Algorithm

An Enhanced Grouping Genetic Algorithm for solving the cell formation problem

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