Manufacturing cell formation using similarity coefficients and a parallel genetic TSP algorithm: Formulation and comparison

Balakrishnan, Jaydeep; Jog, Prasanna

doi:10.1016/0895-7177(95)00092-g

Cited by 35 publications

(10 citation statements)

References 25 publications

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“…This method is further implemented by Venugopal and Narendran [27] to handle multi-objective cell formation problem in which the volume of inter-cell moves and the total within cell moves are minimized. Balakrishna and Jog [28] initially formed the similarity index matrix and then proposed parallel genetic algorithm to solve the cell formation problem. Joines et al [29], Su and Hsu [30] and Mahdavi et al [31] proposed a mathematical model and then implemented a genetic algorithm to minimize the inter-cell moves.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Genetic Algorithm for Machine-Component Cell Formation

Pachayappan¹,

Panneerselvam²

2015

IIM

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This paper considers machine-component cell formation problem of cellular manufacturing system. Since this problem comes under combinatorial category, development of a meta-heuristic is a must. In this paper, a hybrid genetic algorithm is presented. Normally, in genetic algorithm, the initial population is generated by random assignment of genes in each of the chromosomes. In this paper, the initial population is created using ideal seed heuristic. The proposed algorithm is compared with four other algorithms using 28 problems from literature. Through a completed factorial experiment, it is observed that the proposed algorithm outperforms the other algorithms in terms of grouping efficiency as well as grouping efficacy.

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Section: Literature Reviewmentioning

confidence: 99%

Hybrid Genetic Algorithm for Machine-Component Cell Formation

Pachayappan¹,

Panneerselvam²

2015

IIM

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“…The machine‐part cell formation problem has been solved, using, among others, Hamiltonian path and other graph theoretic approaches132–134, integer programming135, fuzzy clustering136, evolutionary approaches13,137,138, traveling salesman problem (TSP)139, neural networks140–143, branch‐and‐bound methods144–146 and with simulated annealing147. However, it seems, unfortunately, that most of the algorithms are not known and acknowledged in the other fields applying two‐mode clustering and other methods to analyze binary datasets.…”

Section: An Historical Overviewmentioning

confidence: 99%

Seriation and matrix reordering methods: An historical overview

Liiv

2010

Statistical Analysis

184

166

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Abstract:Seriation is an exploratory combinatorial data analysis technique to reorder objects into a sequence along a one-dimensional continuum so that it best reveals regularity and patterning among the whole series. Unsupervised learning, using seriation and matrix reordering, allows pattern discovery simultaneously at three information levels: local fragments of relationships, sets of organized local fragments of relationships, and an overall structural pattern. This paper presents an historical overview of seriation and matrix reordering methods, several applications from the following disciplines are included in the retrospective review: archaeology and anthropology; cartography, graphics, and information visualization; sociology and sociometry; psychology and psychometry; ecology; biology and bioinformatics; cellular manufacturing; and operations research.

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“…The first example is taken from Balakrishnan and Jog [2], which is a 12 machines and 19 parts formation problem. The original part-machine incidence is presented in Fig.…”

Section: Illustrative Examplesmentioning

confidence: 99%

Part-machine family formation using genetic algorithms in a fuzzy environment

Pai

Chang

Lee

2004

Int J Adv Manuf Technol

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In traditional cell-formation problems, it is assumed that each part is developed on a single machine. However, in many realistic situations, the suitability of alternative machines for developing the same part feature is frequently vague. Therefore, the fuzzy relation is ideally suited to represent this vague and uncertain cell-formation problem. Furthermore, partmachine family formation problems are usually NP-complete. In this study, a genetic algorithm (GA) is used to deal with the problem. The aim of this study is to design a GA-based algorithm to solve the part-machine family formation problem in a fuzzy environment. The results of computational tests presented are very promising.

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Manufacturing cell formation using similarity coefficients and a parallel genetic TSP algorithm: Formulation and comparison

Cited by 35 publications

References 25 publications

Hybrid Genetic Algorithm for Machine-Component Cell Formation

Hybrid Genetic Algorithm for Machine-Component Cell Formation

Seriation and matrix reordering methods: An historical overview

Part-machine family formation using genetic algorithms in a fuzzy environment

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