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Developing a method to generate semi-distributed layouts by genetic algorithm

delete2012-02-15
delete5
PRE
AI
M
Maryam Hamedi *
N
Napsiah Ismail
G
Gholamreza Esmaeilian
M
Mohd Khairol Anuar Mohd Ariffin
DOI:10.1080/00207543.2010.550329delete
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摘要

摘要

En 中文
Recent research in industries shows that existing layout configurations do not satisfy the needs of multi-product enterprises in turbulent environments but within new layout strategies, distributed layouts have deserved more attention in most manufacturing environments and have a promising potential to cope with demand disturbances. This study is an attempt to design weighted distributed layouts via considering machine independent capabilities by a resource elements (REs) approach, which has caused generation of a new type of distributed layout named semi-distributed layout. REs are used to define processing requirements of parts and processing capabilities of machines. Another contribution of this paper is applying genetic algorithms (GAs) to distribute REs to find the optimal assignment of machines to available locations in such a way the travelled distances of parts are minimised and the accessibility of them to the required machines are maximised. The methodology of this paper is illustrated using a two-phase procedure. First, all machining facilities are divided into a set of REs based on their capabilities and second, the weighted connections among REs are considered to distribute them over the floor through implementing the developed GA. To evaluate the methodology, the proposed algorithm is tested with three illustrative examples obtained from the literature, in which two of them are comparable with outputs of simulated annealing (SA). The comparison between the outputs of the GA and the SA on the same cases presents that for large size problems, the GA significantly outperforms the SA.
Keyword:
semi-distributed layouts
resource element
genetic algorithm
optimisation
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期刊

International Journal of Production Research 封面图
International Journal of Production Research
IF:
7.3
论文数:
1.1W
被引数:
3.7W

机构

U
Universiti Putra Malaysia
学者数:
1.5W
论文数: 1.1W
被引数: 1.4W