A study of the roll planning of fabric spreading using genetic algorithms
In the process of fabric spreading, the variance of fabric yardage between fabric rolls may lead to a difference in fabric loss during spreading. As there are numerous combinations the arrangement of the fabric roll sequences for each cutting lay, it is difficult to construct a roll planning to mini...
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Published in | International journal of clothing science and technology Vol. 12; no. 1; pp. 50 - 62 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Bradford
MCB UP Ltd
01.03.2000
Emerald Group Publishing Limited |
Subjects | |
Online Access | Get full text |
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Summary: | In the process of fabric spreading, the variance of fabric yardage between fabric rolls may lead to a difference in fabric loss during spreading. As there are numerous combinations the arrangement of the fabric roll sequences for each cutting lay, it is difficult to construct a roll planning to minimise the fabric wastage during spreading in apparel manufacturing. Recent advances in computing technology, especially in the area of computational intelligence, can be used to handle this problem. Among the different computational intelligence techniques, genetic algorithms (GA) are particularly suitable. GAs are probabilistic search methods that employ a search technique based on ideas from natural genetics and evolutionary principles. This paper presents the details of GA and explains how the problem of roll planning can be formulated for GA to solve. The result of the study shows that an optimal roll planning can be worked out by using GA approach. It is possible to save a considerable amount of fabric when the best roll planning is used for the production. |
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Bibliography: | original-pdf:0580120104.pdf href:09556220010313832.pdf filenameID:0580120104 ark:/67375/4W2-DL9MDZ4R-V istex:837D2C3C455E0014D8404683E22AB11728AC9B43 ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
ISSN: | 0955-6222 1758-5953 |
DOI: | 10.1108/09556220010313832 |