A Neural Network Approach in Cellular Manufacturing

Mikac, Tonči; Jurković, Milan; Pekic, Z.

doi:10.1007/978-3-7091-2508-3_59

Cited by 2 publications

(1 citation statement)

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“…Machine utilization, grouping efficiency and grouping efficacy was used as an aggregate performance measure of clustering to evaluate the quality of solution found by the Modified Genetic Algorithm for cell formation. Modified genetic algorithm finds out the better quality solution in most cases compared to rank order clustering [17], original ART1 [35], modified ART1 [36]. Modified genetic algorithm finds out the best results to solve cell-formations problem for most simulated examples, or at least same results as found by modified ART1.…”

Section: Modified Genetic Algorithmmentioning

confidence: 99%

Approaches to solve cell formation, machine layout and cell layout problem: A Review

Patel

2014

TMLAI

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Cell formation, machine layout and cell layout problems in cellular manufacturing system are NP-Complete optimization problems. Good cell formation & layout design in cellular manufacturing system is achieved by finding optimum or near-optimum solutions of these problems, which substantially reduces manufacturing cost and time. Many approaches have been advocated by researchers to obtain better cell formation & layout design. An attempt has been done in this paper to review such approaches based on heuristics, meta-heuristics, hybrid methods and exact solution methods developed by past researchers to solve these problems. The main objective of this review paper is to find out the effective and efficient approaches by comparing them based on performance criteria, their benefits and drawbacks in solving cellular manufacturing system problems and find out future research scope in this area.

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Section: Modified Genetic Algorithmmentioning

confidence: 99%