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Erschienen in: Journal of Intelligent Manufacturing 2/2018

02.07.2015

MIP model and elitist strategy hybrid GA–SA algorithm for layout design

verfasst von: I. Jerin Leno, S. Saravana Sankar, S. G. Ponnambalam

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 2/2018

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Abstract

It is most important for any manufacturing industry to have an efficient layout for their production environment to participate in global competition. One of the prime objectives of such an organisation is to decide an optimal arrangement of their facilities (machines or departments) in a two-dimensional planar region satisfying desired objectives, which is termed facility layout problem. To overcome the drawbacks of traditional layout design methodology, it is attempted to solve three important layout design problems such as inter-cell layout design, determination of optimum location for input/output stations and flow path layout design of material handling system simultaneously in an integrated manner. The quality of the final layout is evaluated by minimizing the total material handling cost, where the perimeter distance metric is used for the distance measurement. Sequence-pair, an elegant representation technique is used for layout encoding. The translation from sequence-pair to layout is efficiently done by longest common subsequence computation methodology. Due to the non-polynomial hard nature of the problem considered, an elitist strategy based hybrid genetic algorithm that uses simulated annealing as local search mechanism (ESHGA) is developed and tested with test problem instances available in the literature. The results indicate that proposed integrated methodology with developed mixed integer programming based mathematical model along with ESHGA could generate realistic layouts compared to reported result.

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Metadaten
Titel
MIP model and elitist strategy hybrid GA–SA algorithm for layout design
verfasst von
I. Jerin Leno
S. Saravana Sankar
S. G. Ponnambalam
Publikationsdatum
02.07.2015
Verlag
Springer US
Erschienen in
Journal of Intelligent Manufacturing / Ausgabe 2/2018
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI
https://doi.org/10.1007/s10845-015-1113-x

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